Note: The Coursera courses mentioned in the blog can be audited for free, meaning that you have access to all the course content without any cost. Programming is an essential part of your data science journey. If you know how to code in R, Python, or Julia, it will be quite easy for you to translate algorithms into functions. Moreover, you will learn better techniques to create a program or data reports. I will highly recommend you start with Python and learn the basic syntax and advanced functionalities.

Note: The Coursera courses mentioned in the blog can be audited for free, meaning that you have access to all the course content and can read and view it without any cost. Machine learning is the backbone of modern technology. Almost every big company in the world is trying to use it to get the most out of the data. By taking the free courses, you will learn about classification, regression, clustering, and reinforcement learning. Moreover, you will learn about feature engineering, advanced algorithms, and optimizing techniques.

If you are new to data science, the programming projects will help you get used to syntax, debugging, and learning new tools. Python, R, and Julia are mostly used for data processing, data analysis, machine learning, and research projects. Web scraping is a core part of data engineering and data science, where you collect new data from multiple websites to build a data set for data analysis or machine learning tasks. In general, it is used to create real-time data systems. The analytics project will teach you new tools for data cleaning, processing, and visualization.

Decision Tree Algorithm, Explained by Nagesh Singh Chauhan 21 Cheat Sheets for Data Science Interviews by Nate Rosidi 15 Python Coding Interview Questions You Must Know For Data Science by Nate Rosidi Naïve Bayes Algorithm: Everything You Need to Know by Nagesh Singh Chauhan 14 Essential Git Commands for Data Scientists by Abid Ali Awan Top Programming Languages and Their Uses by Claire D. Costa 3 Ways Understanding Bayes Theorem Will Improve Your Data Science by Nicole Janeway Bills DBSCAN Clustering Algorithm in Machine Learning by Nagesh Singh Chauhan The Complete Collection of Data Science Books – Part 2 by Abid Ali Awan 5 Different Ways to Load Data in Python by Ahmad Anis Top Posts June 13-19: 14 Essential Git Commands for Data Scientists 20 Basic Linux Commands for Data Science Beginners KDnuggets News, June 15: 14 Essential Git Commands for Data Scientists; A… KDnuggets Top Posts for March 2022: Why Are So Many Data Scientists… Top Posts April 4-10: The Complete Collection Of Data Repositories – Part 1 Top Posts March 21-27: Why Are So Many Data Scientists Quitting Their Jobs? Top Posts March 21-27: Why Are So Many Data Scientists Quitting Their Jobs?

Were you in the situation when the interviewer asked you a situational or technical question, and you froze up? Just because you were not prepared for it. It happens to many, including me. I have tendencies to freeze during technical interviews, and the hiring manager will take it as my weakness to reject me at the initial stage of the recruitment process. To overcome this problem, I started to look at sample interview questions.

Decision Tree Algorithm, Explained by Nagesh Singh Chauhan 15 Python Coding Interview Questions You Must Know For Data Science by Nate Rosidi The 6 Python Machine Learning Tools Every Data Scientist Should Know About by Nahla Davies Naïve Bayes Algorithm: Everything You Need to Know by Nagesh Singh Chauhan The Complete Collection of Data Science Books – Part 2 by Abid Ali Awan 21 Cheat Sheets for Data Science Interviews by Nate Rosidi Top Programming Languages and Their Uses by Claire D. Costa The Complete Collection of Data Science Books – Part 1 by Abid Ali Awan 9 Free Harvard Courses to Learn Data Science in 2022 by Natassha Selvaraj DBSCAN Clustering Algorithm in Machine Learning by Nagesh Singh Chauhan

Editor's note: For the full scope of Data Science Books included in this 2 part series, please see The Complete Collection of Data Science Books – Part 1. The data science books have been an influential part of my data science journey. The Deep Learning for Coders with Fastai and PyTorch has made me think outside the box about deep neural networks and how we approach almost any machine learning issue. I am in love with NLP books and how they come with GitHub repositories, Jupyter notebooks exercise, and easy to explore options. Data Science at the Command Line is one of the books that are now available online (documentation style) with the ability to search terms, navigation, and copy the code directly to test the example.

Top Posts April 4-10: The Complete Collection Of Data Repositories – Part 1 Top Posts Mar 14-20: Decision Tree Algorithm, Explained KDnuggets Top Posts for February 2022: The Complete Collection of Data… Top Posts March 21-27: Why Are So Many Data Scientists Quitting Their Jobs? Top Posts March 21-27: Why Are So Many Data Scientists Quitting Their Jobs?

Why Are So Many Data Scientists Quitting Their Jobs? by Natassha Selvaraj Top Posts Dec 20 - Jan 2: 3 Tools to Track and Visualize the Execution of… Top Posts Jan 3-9: Why Do Machine Learning Models Die In Silence? Top Posts Jan 10-16: Is Data Science a Dying Career? Top Posts Jan 3-9: Why Do Machine Learning Models Die In Silence? Top Posts Jan 10-16: Is Data Science a Dying Career?

Editor's note: For the full scope of cheat sheets included in this 2 part series, please see The Complete Collection of Data Science Cheat Sheets - Part 1. Searching for the cheat sheet that works for you can take some time as most of them are not easy to comprehend. The blog contains easy-to-follow and summarized sheet cheats to revise the advanced concepts of data science. The blog series is divided into two parts that includes easy-to-follow and summarized sheet cheats to revise all of the data science concepts. The two part series is further divided into subcategories SQL, Web Scraping, Statistics, Data Analytics, Business Intelligence, Big Data, Data Structures & Algorithms, Machine Learning, Deep Learning, Natural Language Processing, Data Engineering, Web Frameworks, and All in one VIP cheat sheets. The most common technical interview questions are about data structures and algorithms.