Mastering Data Science - A Comprehensive Learning Path

Mastering Data Science : A Comprehensive Learning Path, available at $54.99, has an average rating of 3.93, with 322 lectures, 48 quizzes, based on 7 reviews, and has 79 subscribers.

You will learn about Proficiency in data analysis Good knowledge of Data Visualisation techniques Understanding of machine learning algorithms Basics of Deep Learning, Computer Vision, Statistics and NLP Model Deployment This course is ideal for individuals who are Any data science aspirants It is particularly useful for Any data science aspirants.

Enroll now: Mastering Data Science : A Comprehensive Learning Path

Summary

Title: Mastering Data Science : A Comprehensive Learning Path

Price: $54.99

Average Rating: 3.93

Number of Lectures: 322

Number of Quizzes: 48

Number of Published Lectures: 322

Number of Published Quizzes: 48

Number of Curriculum Items: 370

Number of Published Curriculum Objects: 370

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Proficiency in data analysis

Good knowledge of Data Visualisation techniques

Understanding of machine learning algorithms

Basics of Deep Learning, Computer Vision, Statistics and NLP

Model Deployment

Who Should Attend

Any data science aspirants

Target Audiences

Any data science aspirants

Where do I begin? Data science is such a huge field – where do you even start learning about Data Science?

These are career-defining questions often asked by data science aspirants. There are a million resources out there to refer but the learning journey can be quite exhausting if you don’t know where to start.

Don’t worry, we are here to help you take your first steps into the world of data science! Here’s the learning path for people who want to become a data scientist in 2022. We have arranged and compiled all the best resources in a structured manner so that you have a unified resource to become a successful data scientist.

Moreover, we have added the most in-demand skills for the year 2022 for data scientists including storytelling, model deployment, and much more along with exercises and assignments.

Key takeaways of this course

The course is ideal for beginners in the field of Data Science. Several features which make it exciting are:

Beginner friendly course: This is a beginner-friendly course and has no prerequisites.

Curated list of resources to follow: All the necessary topics are covered in the course, in an orderly manner with links to relevant resources and hackathons.

Updated skillset for 2022: The knowledge of Machine Learning models is important but that won’t set you apart. We have included some of the top unique skills you’ll require to become a data scientist in 2022.

Assignments to test yourself: What’s the best way to test your knowledge? Each module comes with assignments and MCQs to give your memory a boost.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Overview of the Learning Path

Lecture 2: Your Personalized Learning Path for Data Science

Chapter 2: Month 1: Data Science Toolkit

Lecture 1: Plan for Month 1

Lecture 2: Understanding Machine Learning and its impact

Lecture 3: Job of Data Scientist

Lecture 4: Overview of the Course

Lecture 5: A brief introduction to Python

Lecture 6: Installing Python

Lecture 7: Theory of Operators

Lecture 8: Understanding Operators in Python

Lecture 9: Understanding variables and data types

Lecture 10: Variables and Data Types in Python

Lecture 11: Understanding Conditional Statements

Lecture 12: Implementing Conditional Statements in Python

Lecture 13: Understanding Looping Constructs

Lecture 14: Implementing Looping Constructs in Python

Lecture 15: Understanding Functions

Lecture 16: Implementing Functions in Python

Lecture 17: A brief introduction to data structure

Lecture 18: Understanding the concept of Lists

Lecture 19: Implementing Lists in Python

Lecture 20: Understanding the concept of Dictionaries

Lecture 21: Implementing Dictionaries in Python

Lecture 22: Understanding the concept of Standard Libraries

Lecture 23: Reading a CSV File in Python – Introduction to Pandas

Lecture 24: Reading a CSV file in Python: Implementation

Lecture 25: Understanding dataframes and basic operations

Lecture 26: Reading dataframes and conduct basic operations in Python

Lecture 27: Indexing a Dataframe

Lecture 28: Sorting Dataframes

Lecture 29: Merging Dataframes

Lecture 30: Apply function

Lecture 31: Aggregating data

Lecture 32: Basics of Matplotlib

Lecture 33: Data Visualization using Matplotlib

Lecture 34: Basics of Seaborn

Lecture 35: Data Visualization using Seaborn

Lecture 36: Regular Expressions

Lecture 37: Understanding Regular Expressions

Lecture 38: Regular Expressions in Python

Lecture 39: Cheatsheet for Python

Lecture 40: Instructions

Lecture 41: Python Coding Challenge

Chapter 3: Month 2: Data Visualization

Lecture 1: The Power of Visualization

Lecture 2: What is Data Visualization and Why Should we Use it

Lecture 3: Exercise – Definition of Data Visualization

Lecture 4: Hans Rosling – 200 Countries 200 Years 4 Minutes

Lecture 5: 4 Key Elements of Effective Data Visualizations

Lecture 6: Why Tableau is a Powerful Tool for Professionals

Lecture 7: What We Will Cover in this Course

Lecture 8: The Tableau Range of Products

Lecture 9: The 5 Tableau Products you should Know

Lecture 10: Installing Tableau Desktop on your System

Lecture 11: Installing Tableau Public on your System

Lecture 12: Difference Between Tableau Server and Tableau Online

Lecture 13: Navigating the Tableau Interface (Part 1)

Lecture 14: Navigating the Tableau Interface (Part 2)

Lecture 15: Connecting to Data Sources in Tableau

Lecture 16: Understanding the Problem Statement

Lecture 17: Loading the Dataset and Getting Familiar with the Variables

Lecture 18: Build your First Visualization in Tableau!

Lecture 19: Hands-On with Labels and Formatting

Lecture 20: Playing Around with Colors

Lecture 21: Using Filters to Build a Pivot Structure in Tableau

Lecture 22: Exporting your Tableau Worksheet

Lecture 23: The Different Chart Types in Tableau

Lecture 24: Line Charts – Working with Time Series Data

Lecture 25: Building Line Charts in Tableau

Lecture 26: Exercise – Sales of Each Category Month-by-Month

Lecture 27: Generating Map Visualizations for Geospatial Analysis

Lecture 28: Map Visualizations in Tableau

Lecture 29: Exercise – Sales by City Analysis

Instructors

Analytics Vidhya
Data Science Community

Rating Distribution

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4 stars: 4 votes

5 stars: 2 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!