data science career

Dhannush Subramani
| October 19, 2022

This blog will learn about “Data Science career growth in 2022”. It is no longer a secret that today’s economy is entirely dependent on analytics and data-driven solutions/decisions. 

 

Businesses, enterprises, and governments have spent the last few years collecting and analyzing massive volumes of data. If you are interested in the field of Data Science enroll in some Data Science courses offered by reputed Institutions which will be an added advantage during your job hunt. 

 

data science career growth
7 questions everyone asks about data science career growth

 

Data scientists are currently playing a crucial part in the success or failure of any organization, one can even consider choosing a proper Data science certification program which will help learn practically as well as theoretically. Therefore, it is not a stretch to state that “there is a data scientist behind every huge successful company.”

 

Overview of Data Science career

Data science is a fascinating, interesting, intriguing, forward-thinking, and lucrative profession. Importantly, unlike other traditional careers, you do not need an established degree or specialized educational background to begin your journey in Data Science.

 

All you need are the proper abilities, some connected experience, and a curious mind. Considering the need for data scientists in the current market trends indicate that data science course fees are growing.

 

In this blog, I’ll go over the ins and outs of the data scientist job path, as well as the abilities necessary for data Science. In addition, I’ll guide you on how to choose which data science career is best for you.

 

Alright!! Let’s dive into the topics.

 

Table of Contents:

  • What is Data Science?
  • What does a Data Scientist do?
  • Is Data Science right for you?
  • Why choose a career in Data Science?
  • Job statistics in Data Science career
  • Are you ready to become a Data Scientist?

 

What is Data Science?

 

Data science is the study of massive amounts of data using current tools and methodologies to discover previously unknown patterns, extract valuable information, and make business choices. 

 

Data for analysis can come from a wide range of sources and be provided in a variety of ways.

 

Now that you know what data science is, let’s look at what a Data Scientist will do in 2022.

 

What does a Data Scientist do?

 

Data science is a highly interdisciplinary field that works with a broad variety of data and, unlike other analytical fields, focuses on the overall perspective.

data science career
Data scientist working on data – Data Science Dojo

 

In business, the purpose of data science is to give an insight into customers and campaigns, as well as to aid organizations in building effective plans to engage their audiences and sell their products. 

 

Big data, or enormous amounts of information gathered through different methods such as data mining, necessitates the use of creative thinking on the part of data scientists. So, what exactly does a data scientist do?

 

Data scientists use forecasting models to evaluate data and information to produce key insights that help enterprises expand their businesses in the right direction. One of the key responsibilities is to analyze large data sets of quantitative and qualitative data. 

 

This personnel is in charge of developing statistical learning models for data analysis and must be knowledgeable with statistical tools. They must also be knowledgeable enough to create complex prediction models.

Is Data Science right for you?

In my opinion, it is crucial to have an answer to this issue before embarking on your path in data science. Unfortunately, many blogs on the internet indicate that the area of data science is full of demand, great incomes, and respect. 

Nevertheless, the fact is that your journey to data science is not at all easy; it takes continual learning and unlearning of complicated subjects and concepts from different professions, and you must be technically knowledgeable throughout your career.

 

Learn more about Data Science Roadmap 

In this section, I’ll provide you with some suggestions that will take you to the answer to this question. Fundamentally, anyone can acquire and practice any data science skill if they are truly committed to it.

 

Simply said, if you want to learn data science, you can do so.

 

Why choose a career in Data Science?

Data science has been termed the “sexiest job of the twenty-first century.” I’m sure this is a significant role in your decision to pursue a career in data science. Nowadays, any company, large or little, is looking for employees who can interpret and dissect data.

 

Choosing a profession in data science involves respecting the numerous disciplines on which data science as a subject has been founded, such as statistics, math, and technology, among others. The variety of abilities required to become a data scientist might be considered an advantage.

 

Now, let me direct your attention to a few key reasons why you should pursue a career in data science;

 

  • High prestige
  • Be part of future
    Excellent pay
  • Constant challenging work or NO boring work
    Exceptional growth & demand in the market
  • Endless career opportunities

 

Data Science has shown the ability to transform companies and our society. It has become a lucrative job due to a limited supply of trained workers in Data Science and high demand.

 

Job statistics in Data Science career

If you’re here, I’m presuming you’ve picked or are thinking about choosing a career path. Let me direct your attention to a few more key criteria which might assist you in making your final decision.

  • 650% Job growth since 2015 (Via: Linkedin)
  • By 2026, 11.5 million additional jobs are expected to be created (source: U.S. Bureau of Labor Statistics)
  • A data scientist earns an average annual income of $120,931. (source: Glassdoor)
  • In 2020, there are expected to be 2.7 million available positions in data analysis, data science, and related fields (source: IBM).
  • By 2020, there will be a 39% increase in employer demand for both data scientists and data engineers (source IBM).
  • 59% of employment will be in finance, information technology (IT), insurance, and professional services. This is divided as follows: 
  • 19% in banking and insurance, 18% in professional services, and 17% in information technology.
  • Bachelor’s degree holders will be able to apply for 61% of data scientist and advanced analytic roles, while 39% will require a master’s or Ph.D.
  • Positions in data science and data analysis are available for 5 days longer than the average for all jobs, indicating that there is less competition in these professional sectors and recruiters must work harder to locate competent individuals.
  • A possible annual salary of $8,736 more than any other bachelor’s degree position (source: IBM).

 

Pro-Tip: Build up your Data Science career as a licensed Data Scientist

 

The data presented above indicates the development and need for data science specialists across various business areas, geographical regions, and even experience levels. As more businesses implement data-driven solutions, the need for data scientists will continue to rise.

 

So, relax, you’re on the correct track!

 

Are you ready to become a Data Scientist?

Data science is the most in-demand career this decade and will continue to be so in the future. With increased awareness of the industry, competition for positions among professionals is at an all-time high. If you follow this approach and do honest self-evaluation, I am confident you will make the best decision for you.

Enroll in Data Science Bootcamp today to begin your Data Science career

Remember that selecting the proper career path is only the beginning of your journey.

 

Guest Post
| October 12, 2022

In this blog, we will learn the proven successful data science tips to experience exponential growth as a data scientist. There are a few key things that aspiring data scientists should keep in mind if they want to be successful in the field. Let’s learn each tip in detail:

 

1. Learn competitive skills through competitions

Participating in data science competitions is a great way to test your skills and learn from your peers. These competitions will also give you the chance to work on real-world datasets and solve complex problems.  Learn competitive skills through hackathons and Kaggle competitions. Sometimes Kaggle competitions can feel lonely so go to hackathons and build alongside other people to broaden your ideas and get better feedback.

On Kaggle you can learn from some of the best data scientists in the world and participate in interesting competitions with novel datasets to truly build your knowledge and data science expertise. Observable is another free, community-supported place where you can learn a great deal about all things related to data exploration. 

 

2. Develop an understanding of business goals

Data scientists have to be well organized, know statistics, and understand how data work connects to a business objective, not just how to code a model. There’s a popular saying that 85% of modeling projects fail and to beat the odds you have to understand how to connect your model with existing business goals and processes. Usually, this comes with experience and the ability to find creative solutions. 

  

3. Stay calm to tackle the complex data

Expect things to be messy. The data is hardly ever exactly what you need, it can live in many places, and is almost always messier than you thought it would be. It can be hard to estimate how long a project or model will take to build, but I found if you plan and give yourself a one or two-day buffer you’ll find better success with communicating and meeting deadlines. – Ayodele Odubela, Data Scientist, Observable 

 

4. Don’t neglect the basics

It is important to have a strong foundation in mathematics and statistics. This will give you the ability to understand and work with complex data sets. Additionally, it will also allow you to develop sophisticated models and algorithms.

 

5. Choosing the right model

Don’t get too caught up in modeling methods. So many data scientists are constantly worried about choosing the right model, when sometimes a model isn’t needed at all. Sometimes a rules-based system is more applicable, and sometimes a dashboard is the better deliverable for a project. 

 

6. Collaborate with your team

Get more comfortable collaborating with your team. You can optimize your tools so you can cooperate with the least amount of friction. Data scientists often do work for many parts of the business, so reach out to your colleagues to gain better context around the data and how the models you build may be used.  

 

7. Stay up to date with the latest technology

 The field of data science is constantly evolving, with new tools and techniques being developed all the time. As a result, it is important to keep up-to-date with the latest technology. This will ensure that you are able to use the best tools available to solve complex problems.  

 

8. Be creative

Data scientists need to be creative in order to find new ways to solve problems. This means thinking outside of the box and coming up with innovative solutions. Additionally, it is also important to be able to communicate your ideas effectively so that others can understand them.   

 

9. Learn data science through Bootcamps

Bootcamps are another great option for learning data science. These intensive programs will give you the opportunity to learn from experienced data scientists and work on real-world projects.  

 

10. Attend conferences and workshops

Attending conferences and workshops to network with other data scientists and stay up to date with the latest trends in the field. This is also a great opportunity to learn new skills and techniques.   

 

11. Develop strong technical skills

 As a data scientist, you will need to have strong technical skills. This includes expertise in programming languages such as Python and R, as well as experience working with databases and big data platforms. Additionally, you should also be familiar with machine learning algorithms and statistical modeling techniques.   

Technical skills are usually obvious and include core skills such as statistics, programming, mathematics, and data visualization. However, the non-technical skills are equally important if not more so. Chief amongst these is communication skills. If you can’t communicate your findings to the right audience, at the right time, in the right way then it doesn’t matter how good your technical analysis is. 

 

12. Possess business acumen

In addition to technical skills, it is also important to have business acumen. This will allow you to understand the needs of the business and find ways to use data to solve problems. Additionally, being able to effectively communicate with non-technical stakeholders is crucial for success in this role.  

 

13. Be able to use critical thinking

Data scientists need to be able to think critically in order to identify patterns and insights in data. This includes being able to ask the right questions and identify assumptions that need to be tested. Additionally, being able to think creatively is also important for coming up with innovative solutions.   Boris Jabes, Census

 

14. Develop a growth mindset

Developing a growth mindset helps you not to avoid failure and to instead view it as an opportunity to grow. Further, it lets you develop a self-belief that you can learn anything.; fully embrace trying new things, ideas, tools, and techniques; see feedback as a gift that will move you forward and finally to be inspired by the success of others. These attitudes will make an enormous difference to your future success as a data scientist. 

 

 15. Adopt a problem-solving approach 

A data scientist’s job is to solve business problems through data, AI, and ML tools. Data science is problem driven. That means a data scientists need to immerse themselves in learning what the business does and how the business works. Otherwise, the data scientist’s work just because a science experiment in a vacuum. 

  

16. Improve your interpersonal skills

To get anything done, data scientists need access to data. To secure access to data, they need to learn who to ask and how to ask for data. Downloading a dataset from Kaggle is easy. Figuring out who has the previous five years of company sales data, and how to request that data is an underappreciated skill. 

  

17. Evaluate technology on a periodic basis

Never put all your eggs in one tool, one platform, or one framework. Expect technology to change and learn how to adapt to new tools. At the same time, don’t just adopt new tools for the sake of having the latest toys. Do your due diligence and evaluate technology vendors on a periodic basis, to learn which tools are likely to become the next standard, and which are likely to remain niche products. – David, Coda Strategy 

  

18. Prove to be the right fit for the job

 The hiring agents are not only looking for someone having knowledge of data science but someone who is tailor-fitted for the job and one who will produce actual numbers that will be valuable for the company, like sales conversion data, audience engagement data, etc. 

If you look at the US, for example, there’s a need right now for more than 150,000 data scientists. And this need will just grow as we move towards more digital transformation. Aside from the U.S., there’s also a global shortage of data science skills and professionals in Europe and Asia.

It’s also interesting to cite research showing that 94 percent of data scientists and graduates have gotten jobs since 2011. Ninety-four percent is quite encouraging and if you are skilled in data science, you can feel amazingly comfortable that moving in this direction would offer amazing employment potential. This indicates how reliable a career option in data science is now essential moving into the future. 

  

19. Be curious to learn more

Lastly, an intuitive mind and someone with curiosity is what are essential in a data science job. In enormous data sets, valuable data insights are not always obvious, and a trained data scientist needs to have intuition and understand when to go beneath the surface for insightful information. One of the most important soft skills of a data scientist is the ability to ask questions on a regular basis. You can follow all of the processes of the machine learning project lifecycle if you are bored, but you will not be able to attain the final objective and justify your results.

For me, data science is still growing and evolving which means learning in this discipline never ends. One day you master these new tools and have learned a new skill set, and the following day it is run over by a more complex tool and a thirst for another important skill set. So, a data scientist must be inquisitive and always learn to adapt to these rapid changes. Victoria, MediaPeanut

  

20. Know the role you want

There are quite a few distinct roles within data science that are all quite different. Before you enter the career, it can be worth knowing which roles you prefer, and which suits your interests. Talk to people in the industry and ask them about what role they do and who they work with, whether you want to be a data architect or visualization expert you need to know the role suits you. Once you know your role you can fine tune what you need to know and learn to have success in the role. 

  

21. Consider taking a course

Even if you know a lot about data science already, taking a course can help you understand the necessary tools and techniques you need to implement in a specific role. Moreover, many of these courses are work-oriented, as far as they teach you with a career in mind rather than just teaching generic data skills. 

 

21. Build a portfolio

One of the important things to do is practice data analysis and science. Yet rather than just letting go of each project, try to optimize each project to show off your skills. Find a secure place to keep all your projects as your data science portfolio, once you are accepted for an interview you can demonstrate actionable skills for the prospective employer. – Peter, Lantech 

  

22. Work on real-world data science projects

In addition to competitions, another wonderful way to get hands-on experience is by working on real-world data science projects. There are many online repositories (such as GitHub) that contain datasets that you can use to practice your data wrangling, modeling, and visualization skills. Working on projects is also a great way to build your portfolio, which will come in handy when you’re ready to start applying for jobs. – Luke, Ever Wallpaper 

 

23. Obtain the confidence of your peers

As we move about, we assist various teams. We understand that a lot of managers don’t even believe their data. However, they demand brand-new monitors, data science teams, and everything else. But what’s the point? If your data isn’t even reliable. Sherlock Holmes said one of our favorite things:

“Data is the basis for the basic building block of reasoning.”

If such is the case and you have doubts about your home, it will hit you when it drops. Get your superiors to believe in your data and you! 

  

24. Implement a straightforward project with success first

We understand that everyone wants to create the next algorithm for Google or Facebook. Why not? They are hip, incredibly strong, and generate billions of dollars annually. However, if you want your team to flourish and they are just getting started, start small. Don’t worry; even a basic task can offer your executives incomparable value if done correctly. once you’ve achieved your first victory.

The executives will ask you to assist them with everything. You will then need to put in some effort to ensure that either only the proper projects are all being worked on, or that your projects are constantly inundated with requests. 

  

25. Explain the importance of your project

Being a salesman is one technique to garner support from executives. How? Explain the need for the project and create it. Considering how new data science is, many executives are unsure of its benefits and applications. Let them see! That is what you do! Show them how they can employ data science to save time, money, and other resources. – William Drow, Starlinkhow 

 

26. Always give details while requesting assistance

You should always be honest and direct when asking for help, whether it be information, an introduction, or a suggestion. Be direct in your request. People are more willing to help, if you ask them for a modest favor that is not too tough to give. A specific request that is within my sphere of influence makes me far more inclined to say yes when individuals seek my assistance in studying data science.

 

27. It’s important to follow your passions

For many personal and professional reasons, you may be considering a data science career. If, on the other hand, you’re thinking about the financial and social benefits, you should reconsider. Even if the pay and status are decent, working in this field may become challenging if you don’t enjoy it.

Data science initiatives are like any other form of experiment in that not everything turns out perfectly. You also have responsibilities to the company’s shareholders. It’s possible that you won’t always get to work on the kinds of issues that fascinate or excite you. Instead, you’ll probably have to solve issues that benefit your company. – Adam Crossling, Zenzero 

 

Do you have any more successful Data Science tips? Share in the comments 

Data science is a challenging but rewarding field, and I hope these tips have helped you get started on your journey. Remember to keep learning and practicing, and you’ll be well on your way to having a successful career in data science! 

 

 

This blog aims to introduce you to 5 free courses offered by the Data Science Dojo, that can give your data science career a head start

Do you know what skills it takes to get into top data science companies?  A good start to a data science career requires you to be equipped with the tools and techniques currently being used in the industry. Below I have mentioned 5 free courses that will help to get a jump start on your career in data science. 

free courses by data science dojo
Top 5 free courses of data science

A journey from data to diamonds - Data Mining Fundamentals 

Living in a world where everyone is in a rat race for data collection, one should be aware of the fact that just having data is not enough. Your data is probably garbage for your machine learning algorithm. However, a data scientist gives meaning to that data. If you are willing to pursue a data science career path, 70% of your job might just be cleaning and understanding data and making sense of it. This is also called data mining.  

Learn more about Data Mining in this video:

Data Mining Fundamentals” gives you a nice starting point in your job. It takes you on a brief journey of looking at different data types and understanding different issues with the data. Later you get a look into feature engineering and pre-processing that helps to extract the most meaningful information out of the data and that is to be used in machine learning models. Then you jump into the evaluation metrics and statistical methods that provide us with an essential toolkit for extracting meaning from your data. Lastly, you will learn about some useful visualizations that you will need to fully understand your data.  

The clouds that rain data - Introduction to Azure Machine Learning  

We live in a world where machine learning is not just about dummy datasets or solving small-scale problems anymore. Industries are working with ever-changing real-time data that has millions and millions of entries. When working with such large data, it is almost impossible to store and work with it locally. This opens the doors to the world of big data. One of the key skills employers look for in a data science candidate is familiarity with big data and cloud computing.  

The course “Introduction to Azure Machine Learning” provides you with a gateway into the world of big data. You will explore end-to-end data science projects, cover summary statistics and data transformation, and build a machine learning model in Azure ML.  

Data scientists are the new fortune teller - Time Series Analysis and Forecasting  

Have you ever searched the internet for weather predictions for the current week? It is very accurate these days, but it wasn’t always the same case. Data science has given the field of forecasting an immense opportunity to grow. All data consultancies work on the principles of forecasting and future prediction. If you want to pursue a career as a consultant, then time series analysis and forecasting are tools that will put a gold star on your resume.  

The course “Time Series Analysis and Forecasting with Python” helps you get your hands dirty with algorithmic implementations for forecasting using Python. It will give you an introduction to the libraries that are commonly used for forecasting and run you through the whole pipeline that is followed in its course.  

Upgrade your Python skills with Introduction to Data Science with Python.  

Getting your hands dirty with R - Beginner R Programming  

When data scientists walk into an interview, they are expected to walk in with a toolkit. A toolkit that would allow them to build their career. One of the most widely used programming languages in the data science industry is R. R provides one of the best environments for statistical computing with a lot of packages making it very easy and convenient to work with data and use algorithms.  

The course “Beginner R Programming” starts with you creating your first program in R. Then it walks you through the variables, objects, control statements, and functions in R covering all the fundamental knowledge of the subject you would need.  

Data science career with NLP - Introduction to Text Analytics with R  

If a company decides to evaluate the reviews about one of its products. The problem is that there are maybe 100k of them. How do you think they will evaluate them? How do you think a data scientist will address this problem? If you decide to choose a data science career path toward natural language processing, analyzing text and converting words to numbers would be one of your major tasks.  

The course “Introduction to Text Analytics with R” familiarizes you with textual data in the context of machine learning. You will explore different ways to process your data and learn different techniques to prepare it to be used in machine learning models.  

Have you taken these free courses yet?

These data science free courses will guide you on the path of being a functional data scientist. However, if you are interested in exploring the entire data science pipeline and getting a hands-on experience with data mining to big data analytics, you must check out the “Data Science Bootcamp” offered by Data Science Dojo. 

 

Data Science Dojo
| September 28, 2019

This list of 101 top data science interview questions, answers, and key concepts was built to help you prepare and ace your interview.

In October 2012, the Harvard Business Review described “Data Scientist” as the “sexiest” job of the 21st century. Well, as we approach 2020 the description still holds true! The world needs more data scientists than are available for hire. All companies – from the smallest to the biggest – want to hire for a job role that has something “Data” in its name: “Data Scientists”, “Data Analysts”, “Data Engineers” etc.

On the other hand, there’s a large number of people who are trying to get a break in the Data Science industry, including people with considerable experience in other functional domains such as marketing, finance, insurance, and software engineering. You might have already invested in learning data science (maybe even at a Data Science Bootcamp), but how confident are you for your next Data Science interview?

This blog is intended to give you a nice tour of the questions asked in a Data Science interview. After thorough research, we have compiled a list of 101 actual data science interview questions that have been asked between 2016-2019 at some of the largest recruiters in the data science industry – Amazon, Microsoft, Facebook, Google, Netflix, and Expedia, etc.

If you want to know more regarding the tips and tricks for facing a data science interview, watch the AMA with some of our own Data Scientists.

Top categories for best data science interview questions

Data Science is an interdisciplinary field and sits at the intersection of computer science, statistics/mathematics, and domain knowledge. To be able to perform well, one needs to have a good foundation in not one but multiple fields, and it is reflected in the interview. We’ve divided the questions into 6 categories:

  • Machine Learning
  • Data Analysis
  • Statistics, Probability, and Mathematics
  • Programming
  • SQL
  • Experiential/Behavioral Questions

We’ve also provided brief answers and key concepts for each question. Once you’ve gone through all the questions, you’ll have a good understanding of how well you’re prepared for your next data science interview!

Machine learning and data science

As one will expect, data science interviews focus heavily on questions that help the company test your concepts, applications, and experience in machine learning. Each question included in this category has been recently asked in one or more actual data science interviews at companies such as Amazon, Google, Microsoft, etc. These questions will give you a good sense of what sub-topics appear more often than others. You should also pay close attention to the way these questions are phrased in an interview.

Data analysis

Machine learning concepts are not the only area in which you’ll be tested in the interview. Data pre-processing and data exploration are other areas where you can always expect a few questions. We’re grouping all such questions under this category. Data analysis is the process of evaluating data using analytical and statistical tools to discover useful insights. Once again, all these questions have been recently asked in one or more actual data science interviews at the companies listed above.

Statistics, Probability, and Mathematics

As we’ve already mentioned, data science builds its foundation on statistics and probability concepts. Having a strong foundation in statistics and probability concepts is a requirement for data science, and these topics are always brought up in data science interview questions. Here is a list of statistics and probability questions that have been asked in actual data science interviews.

 

Programming

When you appear for a data science interview, your interviewers are not expecting you to come up with a highly efficient code that takes the lowest resources on computer hardware and executes it quickly. However, they do expect you to be able to use R, Python, or SQL programming languages so that you can access the data sources and at least build prototypes for solutions.

You should expect a few programming/coding questions in your data science interviews. Your interviewer might want you to write a short piece of code on a whiteboard to assess how comfortable you are with coding, as well as get a feel for how many lines of codes, you typically write in a given week.

Here are some programming and coding questions that companies like Amazon, Google, and Microsoft have asked in their data science interviews.

Structured Query Language (SQL)

Real-world data is stored in databases and it ‘travels’ via queries. If there’s one language a data science professional must know, it’s SQL – or “Structured Query Language”. SQL is widely used across all job roles in data science and is often a deal-breaker. SQL questions are placed early on in the hiring process and used for screening. Here are some SQL questions that top companies have asked in their data science interviews.

Situational/Behavioral questions

Capabilities don’t necessarily guarantee performance. It’s for this reason employers ask you situational or behavioral questions in order to assess how you would perform in a given situation. In some cases, a situational or behavioral question would force you to reflect on how you behaved and performed in a past situation. A situational question can help interviewers in assessing your role in a project you might have included in your resume, can reveal whether or not you’re a team player, or how you deal with pressure and failure. Situational questions are no less important than any of the technical questions, and it will always help to do some homework beforehand. Recall your experience and be prepared!
Here are some situational/behavioral questions that large tech companies typically ask:

Thanks for reading! We hope this list is able to help you prepare and eventually ace the interview! If you’re still on the job hunt, check out our friends over at Jooble.

If you need help understanding the concepts above, check out Data Science Dojo’s online data science bootcamp!

Like the 101 machine learning algorithms blog post, the accordion drop-down lists are available for you to embed on your own site/blog post. Simply click the ’embed’ button in the lower left-hand corner, copy the iframe, and paste it within the page.

| July 22, 2022

This blog post will provide you with a comprehensive data science roadmap that can aid your learning, helping you succeed in a world loaded with data.

As of 2020, the average salary that a data scientist makes in the US is over $113,000. With that stated, it can be affirmed that data scientists are high in demand. You can think of data science as a way to earn money but then you will never have the actual motivation to learn it. Instead, you should identify a problem; be it marketing-related or a research problem, and then start learning data science & its tools accordingly, because you cannot excel at every tool or a data science skill set. 

First & foremost, you need to motivate yourself to love the data, with no drive you will probably leave your learning journey at some point. Furthermore, you need to work on real projects. Just acquiring the fundamental knowledge or skills won’t make you an expert data scientist, likewise, to increase your expertise, you need to increase the level of difficulty every time you undertake a data science project. While being at work or by joining a top-rated Data Science Bootcamp, learn from your instructors & peers, and check how they are executing the data science projects. Last but not least, present your insights & analysis to others.

But you might be wondering what skills do you exactly require for being a successful data scientist & how to Learn Data Science? What steps do you need to follow to leap into the field of data science?  

Before we get started with the actual data science career path, which of the following expertise/skills do you have?

 

An insight of Data Science roadmap

Since now you have a know-how of what skills you already possess, the roadmap below can help you understand where you stand & what effort is needed for you to reach the endpoint.

Read more about Data Science Career 

Data science roadmap
Comprehensive career guide to data science – Data Science Dojo

Step 1: Getting started 

Before you move on to learning & adapting to new skills, it is important for you to understand what data science is & whether you are a great fit for data science or not.

This article by innoarchitech precisely explains what data science is, it further enlightens on the roles of data scientists, data engineers, and data analysts that can surely help you in deciding which boat to jump in.  

To further assess, check what type of data scientist you are with the below short quiz: 

Step 2: Learn the basics of mathematics & statistics  

The next checkpoint in the data science career path is to learn the fundamentals of mathematics & statistics. The topics listed below should be your area of focus: 

  1. Descriptive Statistics 
  2. Probability  
  3. Inferential Statistics  
  4. Linear Algebra 
  5. Structured Thinking  

This cheat sheet by MIT can help you build your concepts for statistics & likewise here is another cheat sheet by Wzchen that can help you with understanding the basics of probability.  

You can further enrich your concepts with these 5 free statistics books, along with these amazing resources to learn math for data science. If you are wondering why math is needed, then you need to do a quick browse at this blog post by Dave Langer from Data Science Dojo that explains why math is important in data science.  

Step 3: Acquainting the key tools for data science 

1. Python: It is one of the most popular & widely used programming languages. Learning this language can help you with creating web applications, handling big data, rapid prototyping, and much more. To know more about python, check this introductory blog post for it.  

Learn all the fundamentals of Python for Data Science with our upcoming training! 

2. R: Another popular language for programming in R. It provides a free software environment for statistical computing. These few blog posts can definitely add value to your knowledge of R programming:  

  1. Logistics Regression in R
  2. R language programming for Excel Users
  3. Natural language Processing with R programming books

You might be stuck with the same traditional argument between R Versus Python; if you are wondering which one of them you should opt for, then I did suggest you begin with R and transition to Python gradually. Then use them as per the needs of your organization.  

3. Data Exploration & Visualization: If you are into the analytical side of the data i.e. data analysis then you must learn data exploration & visualization. Data exploration is the initial step of data analysis, while, data visualization is the graphical representation of data itself. Both Python & R can be used for exploring & visualizing the data.

Step 4: Learning the key tools for ML 

There exist some basic and advanced machine learning tools that you need to learn & adapt yourself with. Some of the most important ones are listed below. These skills can be of immense value in your overall data science roadmap:  

  1. Exploratory Data Analysis & Data Cleaning: Before moving on to the ML tools, you need to be well versed with what EDA & data cleaning is. EDA or exploratory data analysis is a way of studying the datasets to summarize them into a visual format. Data cleaning is the process of detecting & correcting errors, and ensuring that the data is free of errors.

     The below cheat sheet & the article here can help you get started with EDA now.

EDA cheatsheet for data science professionals
EDA cheat sheet consisting of non-graphical analysis, univariate analysis and multivariate analysis

 2. Feature Selection & Engineering: This should typically be your next step in learning ML. This uses domain knowledge to obtain the features from the data, which in turn helps with improving the performance of ML algorithms. So, if you are willing to gain expertise in the ML domain, you need to learn about feature selection & engineering.

3.  Model Selection: Out of all the statistical models, you will need to select one model that is well-suited for your problem. These are some of the statistical models that you can go with:

A. Linear Regression: It is an algorithm of supervised machine learning, where the slope is constant & the predicted output is continuous. To get started with linear regression, check out this comprehensive cheat sheet by MIT

B. Logistic Regression: It is an algorithm for supervised learning classification that is used to predict the probability of a target variable. It is typically used for classification purposes. This article can be a great resource for you to get started with logistic regression in R. 

C. Decision Trees: This generally uses a decision tree to form assumptions & conclusions about the target values. It is one of the most common approaches of predictive modeling used in statistics & machine learning. 

To build your understanding of a decision tree, this comprehensive tutorial can be of great help to you. 

D. K-Nearest Neighbor (KNN): It is one of the most simple supervised machine learning algorithms that can help with resolving regression & classification problems. It is quite easy to comprehend and learn. But has a few drawbacks

E. K-Means: This is an unsupervised learning algorithm that units the unlabeled sets into diverse clusters. Where K represents the numeral of the centroid. This cheat sheet from Stanford university can help you with learning about K-Means.

F. Naïve Bayes: It is one of the algorithms for supervised learning that helps in solving classification problems. It is considered one of the most successful algorithms because of its nature to create fast ML models can help with making predictions. Here you can find more about Naïve Bayes. 

G. Dimensionality Reduction: A process of transforming the high-dimension space to a low-dimension space to maintain the meaningful properties of data. 

Learning dimensionality reduction is an important skill that every data scientist must possess. Break the curse of dimensionality with Python

H. Random Forests: It is an ensemble learning method for classification, regression, and other task purposes. It includes drawing multiple decision trees at a time & outputting the class that is the mode of all. Dive deep with this amazing guide by Berkley University

I. Gradient Boosting Machines: One of the leading techniques to build predictive models. It helps to deal with regression & classification problems and creates a prediction model in the form of an ensemble of the weak prediction models. 

This guide can help you get started with Gradient Boosting Machines.  

J. XGBOOST: This tool specifically helps with executing the gradient boosted decision trees devised for speed and performance. 

Find answers to what is XGBOOST, how to build an intuition for it, and much more with the guide here

K. Support Vector Machines: These are supervised learning models that are coupled with associated learning, they aid in evaluating the data for regression & classification analysis.  

The below graphic by Avik Jain can be a great help for you to get started with SVMs: 

Support vector machines
Detailed information about support vector machine and tuning parameters

4.  Model Evaluation: Moving towards the last step of machine learning, model evaluation, generalizes the accuracy of the model based on future data. It typically uses two methods, holdout & cross-validation.

Confusion matrix
An image defining the confusion matrix of the classifier

Step 5: Profile building 

Building a profile on GitHub is an important task that every data scientist must complete. It is one of the most effective ways for a data scientist to gather all the code of the projects they have undertaken. It showcases your code and projects undertaken and shows how long you have been practicing data science.  

To get started check this cheat sheet on GitHub

Moving on, you need to be part of some discussion forums. These will help you find an answer to the questions you are stuck at. Here are some of the discussion forums you can be part of: 

  1. Quora  
  2. Stackoverflow 

To gain more knowledge in the data science domain, start following different YouTube channels.   
Our YouTube channel can surely be a good start for you.  

Step 6: Prepare for a data science interview  

You need to know all those key data science concepts that can help you ace your interviews. With these 101 Data Science Interview Questions. Answers, and Key Concepts you can prep up yourself for the interviews.

Step 7: Take a look at a typical data scientist’s job 

Reaching the end of your data science roadmap, you might want to get an idea of a typical data scientist’s job. It is always helpful to look at some job descriptions, showcase your skills, and stand out as the best candidate. If you think you are a good fit for it, you must get started right away!

 

Before I end this post, let me repeat it again, instead of trying to learn all the skills required to be a data scientist endlessly, pick up a problem that inspires you or bees relevant to your domain. Try to solve that problem using the data science skills, only pick up the skills necessary to solve that problem. As you solve more problems, you will learn more skills along the way.

If you hated probability in high school or university, it is because every example of probability has to do with coin tosses and dice. But if you happen to come across interesting problems, such as the Birthday Paradox, you might have ended up loving probability.

Additional support  

Want to learn more about data science roadmap? The following blog posts have been a great support to me, and likewise, I believe it can be a great help to you as well:

So, what have you decided? Are planning to get started with Data Science? Take a look at our Data Science Bootcamp, a great way to start your data science journey.

Muhammad Bilal Awan
| April 13, 2021

Data Science Dojo hosted a data science interview AMA. It was a great opportunity to learn about data science career options, job roles, and skills.

As automated systems replace traditional business processes, a huge amount of data is being generated. Companies all around the world, from publishing houses to health care, are trying to unlock the value of data. Consequently, data science is becoming one of the most sought-after fields for young professionals all over the world.

Recently, Data Science Dojo hosted a data science interview AMA. It was a great opportunity to learn about data science career options and job roles. The webinar also included a Q/A session where data science enthusiasts asked many questions related to data science interviews and careers from the panelists.

The webinar is presented by Data Scientist and Lead Instructor, Rebecca Merrett. She holds a post-graduate diploma in Mathematics and Statistics from the University of Southern Queensland. Co-hosting the webinar is Data Scientist Tarun Shrivas, who is a seasoned professional in Marketing Research and Analytics. He holds a master’s degree in Business Analytics from Seattle University (Seattle, WA).

The webinar begins with a presentation on how to best prepare yourself for the Data Science Industry. The discussion includes the different types of Data Scientists, data science interviews, job roles, commonly used tools, and how to go about building your portfolio. The presentation concludes with about 60 minutes of Q/A. You can watch the video below or continue reading.

* 0:00:00 – Introduction

* 0:01:41 – About Rebecca

* 0:02:22 – About Tarun

* 0:02:50 – Rebecca’s Presentation

* 0:22:23 – Q/A

Entering the field of data science

The Presenters talk about how there’s no right way to building a foundation in data science. You can attend a university, a data science bootcamp, independent mentoring, or even free online courses. Some of these paths will take more effort than others, but one thing was evident, you MUST have a strong understanding of mathematical and statistical concepts.

How to Enter into the Field of Data Science
How to Enter into the Field of Data Science

Types of data science interviews and expectations

Throughout the presentation, emphasis was given to understanding the types of interview questions, job roles, and how candidates can best capitalize on their skillset.

The interviewer is expecting candidates to have knowledge about database tools and have the skills required to read, retrieve, and make sense of the available data. A working knowledge of SQL queries is always helpful as well.

A Data Scientist role also requires candidates to have a fundamental understanding of the following:

  • Conditional probability
  • Bayes theorem
  • Normal and Binomial distribution
  • Central limit theorem
  • Linear Regression

Does this cover everything you should know? No, but these are some of the core subjects in data science. If you’re applying for a role involving product management and analytics, then experience with A/B testing will most likely need to be demonstrated.

Roles available

As we know, data science is a vast field so it’s understandable that there are a variety of job functions available. Following are the three main types of data science roles.

The ‘All-Rounder’ Data Scientist

The Data Scientist is expected to build predictive models which include processing and cleaning data, isolating key features, and collecting new features. Data Scientists should be familiar with big data and machine learning concepts and should be able to drive business decisions.

The ‘Business Facing’ Data Analyst

The Data analyst is expected to visualize and segment data in a way that can help a business gather actionable insights. Data Analyst uses data to understand a key problem, opportunity, or trend that can be utilized in decision making. Data Analysts should be able to transform and manipulate large data sets, produce visualizations, and track web analytics.

The ‘Geeky’ Data Engineer

The Data Engineer is dedicated to deploying analytic solutions in the real world through front-end applications. A Data engineer should be able to set up the infrastructure for large amounts of data and possess strong software engineering skills.

Example Questions

Here are some of the example questions and answers presented at the end of the AMA to give candidates an idea about what to expect and how to best prepare for the interview.

Math & Stats

Example Question: Students’ academic scores follow a normal distribution with a mean of 18 and a standard deviation of 6. What proportion of students have scored between 18 and 24?

To solve this, you should be familiar with the z-score for normal distribution to difference the sample mean from the population mean in proportion to the standard deviation.

Product & Metrics

A company has created a web page to promote a product and encourage signup. One version of the page includes the “Find out more” the other version, “Learn more about us!”. Before going ahead with the second call-to-action, what action would you take to ensure this is the right choice in terms of user signup?

This is a typical A/B test question. You will need to conduct an A/B test with both versions of the page. One audience group will be exposed to version 1 and the other to version 2 so that we can ascertain which version of the page leads to more signups

The important thing is to keep your end goal in mind. If the end goal is the number of signups, then you would prefer the version which leads to a higher proportion of signups even if that page does not get a lot of traffic.

Commonly used tools

The most commonly used tools by data scientists are discussed so that the audience may become familiar with them to build their portfolio.

Here is the list of the most commonly used tools by Data Scientists:

  • R
  • Python
  • Apache Hadoop
  • MapReduce
  • NoSQL Databases
  • Cloud Computing
  • D3
  • Apache Pig
  • Tableau
  • iPython Notebooks
  • GitHub

R and Phyton have the most extensive set of libraries & tools to help and automate everyday tasks. If you’re a Data Engineer, you’re more likely to work with Hadoop, MapReduce, and Spark and as a Data Analyst, interactive data visualizing tools such as Tableau would be frequently used.

Resume tips

The resume is often the first impression your potential employer receives. Therefore, it’s important to carefully design your resume. In the webinar, resume structure and design are discussed in detail.

Structure

You should highlight your strong selling points first. This could be one of your interesting projects which is relevant to the employer. Organizing your resume in the most optimal manner is important to communicate your strong selling points and relevant content.

Design

Keep your resume interesting and to the point. Avoid having multiple pages and lengthy content. Your resume should include contact information and hyperlinks to your projects. It’s a great idea to share content like your website, LinkedIn profile, and other portfolio resources on your resume.

Experience

If you have job experience the important thing is to focus on the results you achieved rather than the actions you took. You want the hiring team to perceive you as result-driven. Be sure to list your experiences in chronological order.

Here are some tips to make your resume stand out:

  • Start bullet points with action verbs where possible.
  • Quantify or state the results of your action where possible.
  • Include Data Science projects and publications.
  • Highlight your business acumen skills.
  • Customize your resume based on the type of job role.
  • Use Resume analyzers: vmockjobscan
  • Check out this data scientist resume guide

What NOT to do in a data science interview?

Tarun and Rebecca explained what not to do in an interview from their own experience interviewing data science candidates. The most important thing is to provide clear examples of your experience with data and statistical analysis if not then your chances of landing the job might be affected. You should provide clear examples of each component of a project you worked with, solving a specific problem, discussing the outcomes of your effort and other activities you were involved in.

Here are few other things to avoid in an interview:

  • Not giving concrete examples of experience with data and statistical analysis.
  • Lack of business acumen.
  • Purely academic or research background.
  • Not asking the right questions.
  • Being too serious. Try your best to make it a pleasant experience for your interviewer.
  • Lack of knowledge about the company.
  • Poor communication skills.
  • Talking in clichés (“I’m a team player”, “I’m a perfectionist”).

Most of those tips apply to candidates applying for a variety of roles. Having knowledge about the company, being practical, building your project portfolio, and improving your communication skills is relevant for most job roles today.

Questions and answers

Attendees posted a few of the questions before the webinar while some of the live questions were also answered. The audience seemed very interested in finding out about data science education and foundation requirements and how to enter the field as a fresh graduate with limited experience.

Q: How to handle LinkedIn invitations from strangers and how to respond to a recruiter reaching out?

The best way to respond to recruiters is to take time composing the reply. You want to present yourself as very interested in the company and their business. You also need to be appreciative of the fact that the recruiter is reaching out to you. You can talk about their products and services, a project they are working on, or any new development which may require new hiring. Present yourself as a potential problem solver for their business.

Q: What are some of the important questions to ask during the data science interview?

You can ask about what kind of data they are working with. the company could be working with highly problematic data and that’s the reason they are hiring an expert. They could be having a data modeling or data management problem. So, it’s a good idea to find out what data problem are they facing. This will give you insight into your day-to-day activities and the job role.

Q: How to answer what are you expecting from this role?

This question is another way of asking how the company fits into your overall career plan. Here you want to justify your current position, maybe you are just entering the field of data science or switching careers or companies. You need to justify why you’re choosing this particular company and the role.

Q: Sharing new ideas with the interviewers about the company would be a good sign?

It is definitely good to share new ideas but keep in mind that first, you need to understand the problem they are having. To propose a solution, you need to have a good understanding of the problem.

Q: How can I answer questions about the most important metrics for an ad marketing campaign?

To answer these questions it is important to have an end goal in mind. Ask yourself what the company is trying to achieve at the end of the day with the help of this metric. For example, if the company is using the number of clicks on a webpage and not considering the end goal of signups then this will not give them a clear picture of the campaign’s success. One of the pages has a 60% click rate but zero signups while the other has only a 20% click rate but a 90% signup rate then, in this case, the latter would be considered more successful. So, answering questions about marketing metrics please keep in mind the end goal.

Q: What is the best thing I can do while in college to land a job in data science after graduating?

The Best thing to do is gain experience, and one of the best ways to gain experience is from community projects. Look for charitable organizations or community organizations that might not have a big budget to hire someone but are willing to have volunteers lead them in the right direction.

For example, an environmental organization looking to collect donations. They have data about different potential cities to set up donation drives. You could conduct population & demographic analysis to find out about the best cities for setting up the donation drives.

Q: There is a lot of competition for entry-level data science jobs. How do you stand out?

Yes, it is challenging especially if you’re talking about the Indian sub-continent. If we talk about the US, then the scenario is different. The number of jobs is abundant compared to the supply of talent, but there’s also another challenge of having the right skill set and experience. Companies are looking to hire individuals with particular skill sets. So it is important to keep improving your skills and gain experience to be able to compete. Having skills other than that of data science can also help to differentiate you from the competition. Try to learn about other business functions to create a more holistic profile.

Q: What are the things that data scientists should keep in mind when searching for his/her first job?

Sometimes it’s better to go for smaller companies as they could provide you more valuable experience. You could really make an impact working for a smaller company as only a few people are running the data science projects. While most of the competition is looking to get into tech giants it might be a good idea to start your career with a smaller company where competition is less, and more opportunities are available to learn and grow.

Q: Do I need Master’s/Ph.D. or an advanced degree to get into data science?

It’s not necessary to get advanced degrees to start your career in data science. Although it’s very important to have a good foundation which you can get from your bachelor’s or some other degree as is the case with most technical fields. But getting advanced degrees does not always guarantee you the best job. It’s equally important to gain experience with community projects, internships, or trainee opportunities. Having a Ph.D. means you have become an excellent researcher and are experienced in working on very difficult problems. This sometimes means opportunities available for the advanced degree holders may be somewhat limited.

Q: Where can you practice machine learning?

Going to hackathons is a good way to practice your machine learning skills in a comfortable setting. It’s also a good environment for guidance and feedback to improve your machine learning skills. You can also start practicing on Kaggle.

Q: What kind of portfolio is required to get into an entry-level Data Science job?

Working on your foundation is very important for entry-level data science jobs. Having a good foundation in mathematics and statistics is required. Being able to understand the metrics and business problems is also required for most data science roles. Understanding linear algebra, conditional probability, Bayes theorem, and central tendencies are necessary. Having a strong foundation helps you with the tools of data science and making analysis. Your portfolio should showcase an understanding of the core concepts and familiarity with some of the commonly used tools.

Q: How to transition from one career to another? For example, from cloud computing development environment to data science or from marketing and automation to data Science or from software engineering to data science.

There are always some transferable skills. If we talk about digital marketing, there is a lot of analytics in this field and requires data science.

If you’re looking at the big production systems, there are many components of software engineering involved. So being skilled in software engineering and data science would be a great advantage. For cloud computing, you can deploy your models if the company is big enough for the heavy-duty infrastructure. You need to find a role where your skills are transferable.

Also, if you are already working somewhere your current organization would be the best place to make the transition into another function. After that, you can definitely look for a company where your preferred role is available and where data science is encouraged.

Q: What’s the interviewer’s approach when hiring fresh data scientists?

Conceptual clarity is very important even if you don’t have years of experience in different data science domains. Make sure whatever you mention in your resume you should be very clear about the concept behind it. The Interviewer will also evaluate your understanding of basic concepts which includes Mathematics, Statistics, and Machine learning. This will give the company sense of how much effort is required to train the candidate.

Q: How do I tell a story about myself and my projects to stand out?

It is very important to provide the interviewer an opportunity to look at the work you have done. For that purpose, you can use the GitHub repository to make your analytics available. Including links to your repository on your resume is a good idea too. Even better is to build a portfolio on WordPress to get noticed.

Portfolio websites are becoming more common nowadays. If you look at the companies hiring pages, they do ask for a LinkedIn profile, GitHub repository, and your website. So, this is a great opportunity to showcase your work efficiently. Your portfolio should not be limited to your code and output only, but should also include some writing sample that describes your output. It’s always a good idea to showcase your communication skills. Most of the time, the hiring person is evaluating if you’re able to clearly communicate your analysis and findings so communication becomes an essential skill.

If you put your work online it becomes easier for the hiring team to research you. So at the time of the interview, they have a better idea of your abilities which could make a big difference.

The webinar was a perfect combination of practical information and guidelines to kick start your career in data science. A great deal of the discussion applies to candidates applying for a role outside of the data science domain.

It’s important for candidates to have a conceptual understanding of the field and demonstrate an interest and understanding of the company they are applying for. To start your career in data science, your first step is to have a strong foundation of the core subjects. The next step is to build your portfolio. Make sure to always be working on your experience. Volunteering for a community project is a great way to practice your skills. Having strong technical skills along with interpersonal and communication skills will help you stand out from the crowd in this highly competitive job market.  Don’t forget about applying for smaller companies. Your role will be more involved, and the lessons you learn from mistakes and successes will be more profound.

Thanks for reading! I hope this has given you a good understanding of data science career options and how to best prepare for an interview. Here is another awesome blog on 101 Data Science Interview Questions to help you get fully prepared for the interview.

Alyse Falk
| November 24, 2021

The field of data science is continuously growing, subsequently, there are a ton of career roles available that one can choose from within the data science domain. This blog lists down some of the most emerging career options that one can opt from.

Data science is a field that requires subject matter expertise (e.g., biology if you plan to do bioinformatics), programming skills, and training in mathematics and statistics. Data science as a service allows companies to get business insights leveraging advanced analytics technologies, including deep learning, without investing in in-house data science competencies.

Data scientists help a company process a huge pool of information from a variety of sources. An expert data science team can help you quickly embrace data science for meeting particular advanced analytics objectives.

Data Science career paths

Glassdoor ranks data science as the #2 job in America for 2021. There are many professions in data science with similar names: for example, ML developers and ML engineers. In this article, we will talk about different roles in data science, how professions in this field differ and what is expected from candidates for different vacancies.

1. Data scientist

The main task of a data scientist is to improve the quality of machine learning models. In general, his or her work can be divided into two blocks. The first one is to work with the finished model in the project. It is necessary to continuously assess its quality and find what can be improved. Online and offline metrics, as well as feedback from testers, help in this. The second is the research part itself: finding new architectures and signals for prediction.

Here is what a data scientist needs to know:

  • Python to develop models.

  • C++ to put code into production.

  • Deep learning frameworks (TensorFlow, PyTorch, Caffe, or others).

  • Data structures and algorithms.

A lot of time data time collecting, cleaning, and analyzing data for useful insights. After preparing the data, they spend the rest of the time training new models e.g., preparing data on a cluster and writing infrastructure for effective training.

It’s also part of the job to duplicate the model: you have to write the model and check that it behaves as expected on real data, and then optimize its performance. An interesting fact is that the job of a data scientist also involves skills from the roles of ML engineer, data engineer, and data scientist.

PRO TIP: Join our data science bootcamp today to kickstart a successful career in data science!

2. ML engineer

The responsibilities of an ML engineer are remarkably similar to a data scientist. But in contrast, there is no need to prepare publications in scientific journals and continually develop modern technologies. Much more important than for a data scientist is the ability to write effective and readable code that colleagues can then make sense of.

Here is what an ML engineer needs to know:

  • Python and C++ to develop models and train algorithms.

  • Probability theory, statistics, and discrete mathematics.

  • Deep Learning frameworks (TensorFlow, PyTorch, Caffe, or others).

It is also useful for ML engineers to have collaborative development tools. They should be able to not only train high-quality models but should also be able to create services based on them that can withstand a high load. This may require mastery of both lower-level programming languages and techniques for optimizing machine learning models.

Data engineers are in charge of preparing data for subsequent analysis. Their job is to first gather data from social networks, websites, blogs, and other external and internal sources, and then bring it into a structured form that can be sent to the data analyst.

3. Data engineer

Imagine you need to make an apple pie. First, you need to find the flour, apple, eggs, milk, and other ingredients from the recipe. That’s what the data engineer does, just looking for and bringing in the right data. And the data analyst will make the pie himself, or rather look for patterns among the found data.

Here is what a data engineer needs to know:

  • How to design storage, set up data collection, and data pipelines.

  • How to build ETL processes.

  • C++, Python, or Java.

  • SQL for working with databases.

In addition, engineers create and maintain the storage infrastructure. They are also responsible for the ETL system – extracting, transforming, and loading data into one repository. It is safe to say that they are responsible for buying and storing the ingredients for the pie. Thus, the data analyst can pick them up any time to make a dish and be sure that everything is in place and nothing has gone bad.

4. Data analyst

Data analysts help a company improve metrics and solve intermediate goals rather than blindly move toward big goals (doubling revenue in a year). More often than not, they work closely with salespeople.

The task of the data analyst is to process a large amount of data and find patterns in it. For example, they may find out that most often toothbrushes are bought by married men from 30 to 40 years old. Data analysts help companies better understand their customers and, which in-turn brings in more sales.

Here is what a data analyst needs to know:

  • Python to process data.

  • Mathematical statistics to choose the right methods to process the data.

  • SQL dialects like ClickHouse.

  • DataLens, Tableau, PowerBI, and other dashboard tools.

  • Big data tools such as Hadoop, Hive, or Spark.

In their work, data analysts use the knowledge of mathematical statistics, which allows them to find patterns and assist with predicting the behavior of users. Data analysts also conduct tests, check how users react to the new interface, and help optimize business processes.

Summary

There are many directions and tasks to learn data science for those who like exact sciences. You can do science-intensive tasks as a data scientist, implement innovative technologies as an ML engineer, look for useful patterns for business as a data analyst, or collect and structure data if you choose to work as a data engineer. In addition, your choices can be based not only on your expertise but also on the problems you want to solve: you dream of moving science forward and creating technology that others will use.

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