*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

**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:

- Descriptive Statistics
- Probability
- Inferential Statistics
- Linear Algebra
- 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:

**Logistics Regression in R****R language programming for Excel Users****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:

- 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.

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:

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.

**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:

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:

**Essential Data Science Job Skills Every Data Scientist Should Know**

B.

**Best Places to Work as a Data Scientist Around the World**

*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**.*