Data Science is now a requisite skill for a variety of job roles in the world of business. Be it Marketing, Sales, Finance, HR – Data Science has permeated every business domain. Not just the fresh breed of graduates but top level CxOs are undergoing training to acquire this skill. DataCamp, one of the leaders in Data Science online courses, has an extremely well laid-out career track which takes you from the basics to the expert level concepts in Data Science in a structured manner.
This ‘Data Scientist with Python’ career track by Python has become the first choice for many new learners for learning Python for Data Science. In this Study Plan for DataCamp’s Data Scientist with Python Career Track, learn the skills to become an expert data scientist through guided learning material. The book will take you through all the modules of DataCamp’s ‘Data Scientist with Python’ career track with the help of additional video and reference links.
About the Author
Founder, Super Heuristics
B.Tech, ECE | MBA, IIM Udaipur
Darpan Saxena, the founder of Super Heuristics, is a Marketing Strategist and a practitioner of Analytics in the field of Marketing. He is an Electronics & Communication engineer by qualification, with specialization in Artificial Intelligence (AI) focusing on Machine Learning, and also an MBA from Indian Institute of Management (IIM), Udaipur.
Super Heuristics is the best place for students and young professionals to learn Marketing & Analytics. Super Heuristics was founded in February 2018 by Darpan Saxena.
In his 6+ years of professional experience, he has crafted go-to-market strategies for brands like Abbott (in Singapore), Genpact and CL Educate apart from the other small and medium businesses which have witnessed growth through his marketing and strategy consultation. Darpan has worked as a Product Head of the biggest vertical of an education technology company in New Delhi.
Who is it for?
While Data Science is for everyone, literally everyone, Data Science is definitely a must for the following types of learners.
Entry-level executives in any domain such as Marketing, Sales, Finance, Operations, HR in any industry
Undergraduate students looking to break away from the competition to bag jobs of the future in the field of Data Science & Machine Learning
Postgraduate and MBA students willing to deep-dive into Data Science and acquire this skill to take a big career leap
Mid- and Senior-level management with the responsibility of bringing in new thought-leadership for a progressive best practices
5 Reasons to get started with this study plan
- Easiest way to get started with the seemingly overwhelming Data Scientist with Python Career Track of DataCamp
- Lucid explanation of basic and advanced concepts discussed in DataCamp’s Data Scientist with Python Career Track
- Not feeling like going through the course? Read this book and browse through additional study material linked in the book
- Serves as an extremely helpful revision tool. Watch a DataCamp video and get back to the book to solidify your learning
- No need to have an internet connection all the time to learn Data Science as the book explains the concepts in a self-explanatory manner
Table of Contents
This study plan aims at making you learn Python through DataCamp without having you feel overwhelmed with the volume that is to be covered. The study plan is in-sync with the Data Science with Python Career track of DataCamp and covers material on all of the topics mentioned there.
- Lesson 1: Introduction to Python
- Lesson 2: Intermediate Python
- Project: TV, Halftime Shows and the Big Game
- Lesson 4: Data Manipulation with pandas
- Project: The Android App Market on Google Play
- Lesson 6: Merging DataFrames with pandas
- Project: The GitHub History of the Scala Language
- Lesson 8: Introduction to Data Visualization with Matplotlib
- Lesson 9: Introduction to Data Visualization with Seaborn
- Lesson 10: Python Data Science Toolbox (Part 1)
- Lesson 11: Python Data Science Toolbox (Part 2)
- Lesson 12: Intermediate Data Visualization with Seaborn
- Project: AVisual History of Nobel Prize Winners
- Lesson 14: Introduction to Importing Data in Python
- Lesson 15: Intermediate Importing Data in Python
- Lesson 16: Cleaning Data in Python
- Lesson 17: Working with Dates and Times in Python
- Lesson 18: Exploratory Data Analysis in Python
- Lesson 19: Analyzing Police Activity with pandas
- Lesson 20: Statistical Thinking in Python (Part 1)
- Lesson 21: Statistical Thinking in Python (Part 2)
- Project: Dr. Semmelweis and the Discovery of Handwashing
- Lesson 23: Supervised Learning with scikit-learn
- Project: Predicting Credit Card Approvals
- Lesson 25: Unsupervised Learning
- Lesson 26: MLwith Tree Based Models
- Case Study: School Budgeting with Machine Learning in Python
- Lesson 28: Cluster Analysis with Python