fbpx
Learn to build large language model applications: vector databases, langchain, fine tuning and prompt engineering. Learn more

data science course

Get a behind-the-scenes look at Data Science Dojo’s intensive data science Bootcamp. Learn about the course curriculum, instructor quality, and overall experience in our comprehensive review.

“The more I learn, the more I realize what I don’t know”

(A quote by Raja Iqbal, CEO of DS-Dojo)

In our current era, the terms “AI”, “ML”, “analytics”–etc., are indeed THE “buzzwords” du jour. And yes, these interdisciplinary subjects/topics are **very** important, given our ever-increasing computing capabilities, big-data systems, etc. 

The problem, however, is that **very few** folks know how to teach these concepts! But to be fair, teaching in general–even for the easiest subjects–is hard. In any case, **this**–the ability to effectively teach the concepts of data-science–is the genius of DS-Dojo. Raja and his team make these concepts considerably easy to grasp and practice, giving students both a “big picture-,” as well as a minutiae-level understanding of many of the necessary details. 

Learn more about the Data Science Bootcamp course offered by Data Science Dojo

Still, a leery prospective student might wonder if the program is worth their time, effort, and financial resources. In the sections below, I attempt to address this concern, elaborating on some of the unique value propositions of DS-Dojo’s pedagogical methods.

Data Science Bootcamp Review - Data Science Dojo
Data Science Bootcamp Review – Data Science Dojo

The More Things Change

Data Science enthusiasts today might not realize it, but many of the techniques–in their basic or other forms–have been around for decades. Thus, before diving into the details of data-science processes, students are reminded that long before the terms “big data,” AI/ML, and others became popularized, various industries had all utilized techniques similar to many of today’s data-science models. These include (among others): insurance, search engines, online shopping portals, and social networks. 

This exposure helps Data-Science Dojo students consider the numerous creative ways of gathering and using big data from various sources–i.e. directly from human activities or information, or from digital footprints or byproducts of our use of online technologies.

 

The Big Picture of the Data Science Bootcamp

As for the main curriculum contents, first, DS-Dojo students learn the basics of data exploration, processing/cleaning, and engineering. Students are also taught how to tell stories with data. After all, without predictive or prescriptive–and other–insights, big data is useless.

The bootcamp also stresses the importance of domain knowledge, and relatedly, an awareness of what precise data points should be sought and analyzed. DS-Dojo also trains students to critically assess: why, and how should we classify data. Students also learn the typical data-collection, processing, and analysis pipeline, i.e.:

  1. Influx
  2. Collection
  3. Preprocessing
  4. Transformation
  5. Data-mining
  6. And finally, interpretation and evaluation.

However, any aspiring (good) data scientist should disabuse themselves of the notion that the process doesn’t present challenges. Au contraire, there are numerous challenges; e.g. (among others):

  1. Scalability
  2. Dimensionality
  3. Complex and heterogeneous data
  4. Data quality
  5. Data ownership and distribution, 
  6. Privacy, 
  7. Reaction time.

 

Deep dives

Following the above coverage of the craft’s introductory processes and challenges, DS-Dojo students are then led earnestly into the deeper ends of data-science characteristics and features. For instance, vis-a-vis predictive analytics, how should a data-scientist decide when to use unsupervised learning, versus supervised learning? Among other considerations, practitioners can decide using the criteria listed below.

 

Unsupervised Learning…Vs. … >> << …Vs. …Supervised Learning
>> Target values unknown >> Targets known
>> Training data unlabeled >> Data labeled
>> Goal: discover information hidden in the data >> Goal: Find a way to map attributes to target value(s)
>> Clustering >> Classification and regression

 

Read more about the supervised and unsupervised learning

 

Overall, the main domains covered by DS-Dojo’s data-science bootcamp curriculum are:

  • An introduction/overview of the field, including the above-described “big picture,” as well as visualization, and an emphasis on story-telling–or, stated differently, the retrieval of actual/real insights from data;
  • Overview of classification processes and tools
  •  Applications of classification
  • Unsupervised learning; 
  • Regression;
  • Special topics–e.g., text-analysis
  • And “last but [certainly] not least,” big-data engineering and distribution systems. 

 

Method-/Tool-Abstraction

In addition to the above-described advantageous traits, data-science enthusiasts, aspirants, and practitioners who join this program will be pleasantly surprised with the bootcamp’s de-emphasis on specific tools/approaches.  In other words, instead of using doctrinaire approaches that favor only Python, R, Azure, etc., DS-Dojo emphasizes the need for pragmatism; practitioners should embrace the variety of tools at their disposal.

“Whoo-Hoo! Yes, I’m a Data Scientist!”

By the end of the bootcamp, students might be tempted to adopt the above stance–i.e., as stated above (as this section’s title/subheading). But as a proud alumnus of the program, I would cautiously respond: “Maybe!” And if you have indeed mastered the concepts and tools, congratulations!

But strive to remember that the most passionate data science practitioners possess a rather paradoxical trait: humility, and an openness to lifelong learning. As Raja Iqbal, CEO of DS-Dojo pointed out in one of the earlier lectures: The more I learn, the more I realize what I don’t know. Happy data-crunching!

 

register now

January 6, 2023

In this blog, we will discuss how companies apply data science in business and use combinations of multiple disciplines such as statistics, data analysis, and machine learning to analyze data and extract knowledge. 

If you are a beginner or a professional seeking to learn more about concepts like Machine Learning, Deep Learning, and Neural Networks, the overview of these videos will help you develop your basic understanding of Data Science.  

data science free course
List of data science free courses

Overview of the data science course for beginners 

If you are an aspiring data scientist, it is essential for you to understand the business problem first. It allows you to set the right direction for your data science project to achieve business goals.  

As you are assigned a data science project, you must assure yourself to gather relevant information around the scope of the project. For that you must perform three steps: 

  1. Ask relevant questions from the client 
  2. Understand the objectives of the project 
  3. Defines the problem that needs to be tackled 

As you are now aware of the business problem, the next step is to perform data acquisition. Data is gathered from multiple sources such as: 

  • Web servers 
  • Logs 
  • Databases 
  • APIs 
  • Online repositories 

1. Getting Started with Python and R for Data Science 

Python is an open source, high-level, object-oriented programming language that is widely used for web development and data science. It is a perfect fit for data analysis and machine learning tasks, as it is easy to learn and offers a wide range of tools and features.  

Python is a flexible language that can be used for a variety of tasks, including data analysis, programming, and web development. Python is an ideal tool for data scientists who are looking to learn more about data analysis and machine learning. 
 

Getting started with Python and R for Data Science

 

Python is a great choice for beginners as well as experienced developers who are looking to expand their skill set. Python is an ideal language for data scientists who are looking to learn more about data analysis and machine learning. It is used to accomplish a variety of tasks, including data analysis, programming, and web development.  

Python is an ideal tool for data scientists who are looking to learn more about data analysis and machine learning. Python is a great choice for beginners as well as experienced developers who are looking to expand their skill set.  

2. Intro to Big Data, Data Science & Predictive Analytics 

Big data is a term that has been around for a few years now, and it has become increasingly important for businesses to understand what it is and how it can be used. Big data is basically any data that is too large to be stored on a single computer or server and instead needs to be spread across many different computers and servers in order to be processed and analyzed.  

The main benefits of big data are that it allows businesses to gain a greater understanding of their customers and the products they are interested in, which allows them to make better decisions about how to market and sell their products. In addition, big data also allows businesses to take advantage of artificial intelligence (AI) technology, which can allow them to make predictions about the future based on the data they are collecting. 

Intro to Big Data, Data Science & Predictive Analytics 

The main areas that businesses need to be aware of when they start using big data are security and privacy. Big data can be extremely dangerous if it is not properly protected, as it can allow anyone with access to the data to see the information that is being collected. In addition, big data can also be extremely dangerous if it is not properly anonymized, as it can allow anyone with access to the data to see the information that is being collected. 

One of the best ways to protect your data is by using encryption technology. Encryption allows you to hide your data from anyone who does not have access to it, so you can ensure that no one but you have access to your data. However, encryption does not protect 

 3. Intro to Azure ML & Cloud Computing 

Cloud computing is a growing trend in IT that allows organizations to perform delivery of computing services including servers, storage, databases, networking, software, analytics, and intelligence. Cloud offers a number of benefits, including reduced costs and increased flexibility.  

Organizations can take advantage of the power of the cloud to reduce their costs and increase flexibility, while still being able to stay up to date with new technology. In addition, organizations can take advantage of the flexibility offered by the cloud to quickly adopt new technologies and stay competitive. 

Intro to Azure ML & Cloud Computing 

In this intro to Azure Machine learning & Cloud Computing, we’ll cover some of the key benefits of using Azure and how it can help organizations get started with machine learning and cloud computing. We’ll also cover some of the key tools that are available in Azure to help you get started with your machine learning and cloud computing projects. 

 

Start your Data Science journey today 

If you are afraid of spending hundreds of dollars to enroll in a data science course, then direct yourself to the hundreds of free videos available online. Master your Data Science learning and step into the world of advanced technology. 

November 8, 2022

Related Topics

Statistics
Resources
Programming
Machine Learning
LLM
Generative AI
Data Visualization
Data Security
Data Science
Data Engineering
Data Analytics
Computer Vision
Career
Artificial Intelligence