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About Introduction to Data Science Course

Why pursue Data Science:

  • Data Science is the hottest field in the industry right now
  • Data Scientists are one of the most demanded professionals
  • There are so many data science algorithms to build predictive models, such as linear regression, logistic regression, decision trees and random forests. Keep learning, keep growing!
  • The potential of data science is limitless - spanning across industries, roles and functions


What will you learn in the ‘Introduction to Data Science’ course?

  • Understand what data science is
  • The various and diverse applications of data science
  • Common data science terminologies
  • Python for data science – the most popular data science programming language
    • Python from scratch – no previous programming experience required!
    • Understand how to use Jupyter Notebooks for data science
    • Get familiar with popular Python libraries for data science like Pandas and NumPy
  • Core Statistics for data science - Descriptive Statistics and Inferential Statistics
    • Measures of central tendency - mean, median, mode
    • Standard deviation
    • Outlier detection
    • Correlation
    • Hypothesis Testing
    • Confidence Intervals & margin of error
    • Chi-squared test
  • Probability concepts for data science
    • Introduction to Probability
    • Central Limit Theorem
    • Bernoulli Trials
  • Introduction to core machine learning algorithms for data science
    • Types of predictive models in data science
    • Overview of end-to-end data science process
    • Data extraction and data cleaning
    • Basics of machine learning model building
    • Linear regression
    • Logistic regression
    • Decision trees
    • Random forests
    • And much, much more!
  • Plenty of hands-on examples and multiple real-world industry-relevant data science projects

Why take Introduction to Data Science course?

Ideal way to start your Data Science Journey
Introduction to Data Science course has been created keeping a data science beginner in mind. This course provides everything you need to start your journey in data science.

Easy to understand content
The biggest challenge beginners face is that most of the courses explain data science as a difficult mathematical subject. Not us! We simplify data science and machine learning with easy to understand videos and help you build intuition on data science concepts.

Experienced Instructors
All the material in this course was created by Analytics Vidhya instructors who bring in immense experience of data science with them. All our instructors have years of experience in data science and analytics.

Industry collaboration
Entire course has been vetted and created along with experts from data science industry. This ensures relevance in industry and enabling you with the content which matters the most.

Real life projects and problems
All projects in the course are based on real life data science problems. No academic datasets are bring used to ensure that you are ready for real life problems.

Pre-requisites

This course requires  no past knowledge about Data Science or any tool. 

Highlights of Introduction to Data Science Course

  • 6 Hours of easy to understand videos

    Covering Introduction to Python, Statistics, Predictive Models and Data Science fundamentals

  • 4 Real Life Projects from Data Science Industry

    To prepare you for Data Science Career and Industry

  • Live Q & A Session

    Every Thursday 9 pm to 10 pm (IST)

Introduction to Data Science Course Curriculum

  • 1
    Course Handouts
    • Course Handouts
  • 2
    Introduction to Data Science
  • 3
    Basic Python for Data Science
    • Brief Introduction to Python
    • Introduction to Python test
    • Installation steps for Mac
    • Installation steps for Linux
    • Installation steps for Windows
    • Theory of Operators
    • Exercise
    • Understanding Operators in Python
    • Operators test
    • Understanding variables and data types
    • Variables test
    • Variables and Data Types in Python
    • Exercise
    • Understanding Conditional Statements
    • Exercise
    • Implementing Conditional Statements in Python
    • Conditional Statements test
    • Understanding Looping Constructs FREE PREVIEW
    • Exercise
    • Implementing Looping Constructs in Python
    • Looping Constructs test
    • Understanding Functions
    • Exercise
    • Implementing Functions in Python
    • Functions test
    • A brief introduction to data structure
    • Data Structure test
    • Understanding the concept of Lists
    • Understanding the concept of Lists-Reference
    • Lists test
    • Implementing Lists in Python
    • Exercise
    • Understanding the concept of Dictionaries
    • Exercise
    • Implementing Dictionaries in Python
    • Dictionaries test
    • Understanding the concept of Standard Libraries
    • Libraries test
    • Reading a CSV File in Python - Introduction to Pandas
    • Reading a CSV file in Python - Implementation
    • Reading a csv file in Python test
    • Understanding dataframes and basic operations
    • DataFrames and basic operations test
    • Reading dataframes and conduct basic operations in Python
    • Reading dataframes and conduct basic operations in Python Test
    • Indexing a Dataframe
    • Indexing DataFrames test
    • Exercise
    • Regular Expressions
    • Instructions
    • Quiz
    • Python Coding Challenge
  • 4
    Understanding Statistics for Data Science
    • Introduction to statistics
    • Mode of the data
    • Mode test
    • Understanding the various variable types
    • Understanding Variable Types test
    • Mean of the data
    • Mean test
    • Outliers in the datasets
    • Outlier test
    • Median of the dataset
    • Median test
    • Spread of the data
    • Spread test
    • Variance of the data
    • Variance test
    • Standard Deviation of the data
    • Standard Deviation test
    • Frequency Tables
    • Frequency Tables test
    • Histograms
    • Histograms test
    • Introduction to Probability
    • Introduction to probability test
    • Calculating Probabilities of events
    • Calculating Probabilities test
    • Bernoulli Trials and Probability Mass Function
    • Bernoulli Trials and PMF test
    • Probabilities for Continuous Random Variables
    • Probabilities for continuous random variable test
    • The Central Limit Theorem
    • Central Limit Theorem test
    • Properties of the Normal Distribution
    • Properties of Normal distribution test
    • Using the Normal Curve for Calculations
    • Normal Curve for calculations test
    • Z score Part 1
    • Understanding the Z tables
    • Z scores test
    • Z score part 2
    • Introduction to Inferential Statistics
    • Introduction to Inferential Statistics test
    • Short Review
    • Review test
    • Mean Estimation
    • Confidence Interval and Margin of Error
    • CI and Margin of error test
    • Introduction to Hypothesis Testing
    • Hypothesis testing test
    • Steps to perform hypothesis testing
    • Directional Non Directional hypothesis
    • Directional and Non Directional hypothesis test
    • Understanding Errors while Hypothesis Testing
    • Errors while Hypothesis testing test
    • Understanding T tests
    • Understanding T tests - test
    • Degree of Freedom
    • T-Critical Value
    • T-Critical Value Test
    • Steps to perform T-Test
    • Steps to perform T-Test test
    • Conducting One sample T test
    • One sample T tests - test
    • Paired T tests
    • Paired T tests - test
    • 2 Sample T tests
    • 2 sample T tests - test
    • Chi Squared Tests
    • Chi squared tests - test
    • Correlation
    • Correlation test
    • Conclusion
    • Module Test
    • Instructions
    • Quiz
    • Statistics Coding Challenge
  • 5
    Class Recordings
    • 18 Apr 19: Part1
    • 18 Apr 19: Part2
    • 18 Apr 19: Part3
    • 02 May 19
    • 09 May 2019 Part1
    • 09 May 2019 Part2
    • 10 May 2019 Part1
    • 10 May 2019 Part2
    • 13 May 2019 Part1
    • 13 May 2019 Part2
    • 14 May 2019 Part1
    • 14 May 2019 Part2
    • 15 May 2019
    • 04 Jun 2019
    • 06 Jun 2019
    • 07 Jun 2019
    • 10 Jun 2019
    • 11 Jun 2019
    • 12 Jun 2019
    • 13 Jun 2019
    • 18 Jun 2019
    • 20 Jun 2019
    • 25 Jun 2019
    • 27 Jun 2019
    • 28 Jun 2019
    • 01 Jul 2019 Part1
    • 01 Jul 2019 Part2
    • 02 Jul 2019
    • 03 Jul 2019
    • 04 Jul 2019
    • 05 Jul 2019
    • 08 Jul 2019
  • 6
    Datasets
    • Regression Datasets, Jupyter Notebook & Images
    • Decision Tree Datasets
    • Ensemble Models
    • Notes of Big Data Class
    • Notes of Big Data Class
    • PDF's for Big Data Classes
    • Machine Learning Notes
  • 7
    Big Data Videos
    • Session 1 - Flume
    • Session 2 - Basics of RDD
    • Session 3 - Key Values RDD Part 1
    • Session 4 - Key Values RDD Part 2
    • Session 5 - SparkSQL, Dataframes, SparkR
    • Session 6 - Dataframes and SparkSQL
    • Session 7 - Dataframes and Spark SQL
    • Session 8 - Spark SQL, Dataframes, Spark R
    • Session 9 - Spark Streaming
    • Session 10 - Spark Streaming, Kafka
    • Session 11 - Spark Streaming
    • Session 12 - Apache Spark, GraphX
    • Session 13 - Spark Streaming
    • Session 14 - Machine Learning with MLlib

Project - Identify best Insurance agents

Predict performance of Insurance agents using past data

Your client is looking for help from data scientists like you to help them provide business insigths using their past recruitment data. They want you to predict the target variable for each potential agent, which would help them identify the right agents to hire.
Project - Identify best Insurance agents

Project - Sales Prediction for a large Supermarket

Use Data Science to predict sales of products across Supermarkets

The data scientists at BigMart have collected sales data for 1559 products across 10 stores in different cities for an entire year. Also, certain attributes of each product and store have been defined. You will build a predictive model to forecast the sales of each product at a particular store.
Project - Sales Prediction for a large Supermarket

Project - Predict survivors from Titanic tragedy (In-class)

Use data science to identify survivors

You will analyse what kind of people were likely to survive in Titanic tragedy. You will apply machine learning algorithms to predict which passengers survived the tragedy.
Project - Predict survivors from Titanic tragedy (In-class)

Instructor(s)

  • Kunal Jain

    Founder & CEO

    Kunal Jain

    Kunal is the Founder of Analytics Vidhya. Analytics Vidhya is one of largest Data Science community across the globe. Kunal is a data science evangelist and has a passion for teaching practical machine learning and data science. Before starting Analytics Vidhya, Kunal had worked in Analytics and Data Science for more than 12 years across various geographies and companies like Capital One and Aviva Life Insurance. He has worked with several clients and helped them build their data science capabilities from scratch.
  • Neeraj Singh Sarwan

    Neeraj Singh Sarwan

    Neeraj is working as a data scientist at Analytics Vidhya. He has extensive experience in converting business problems to data problems. He has previously taken several corporate trainings and is also an avid blogger. He's a graduate of IIT-BHU and will be your instructor for the Python and Modeling modules.
  • Pranav  Dar

    Pranav Dar

    Pranav is the editor for Analytics Vidhya. He has experience in data visualization and has been previously involved in the learning & development departments of MNCs. He has taken various trainings on statistics & presentation skills and loves to read, write about anything from data science to machine learning. He will be your instructor for the statistics module.

FAQ

  • Who should take this course?

    This course is meant for people looking to learn Data Science. We will start out to understand the pre-requisites, and then go on to solve case studies using Data Science concepts.

  • When will the classes be held in this course?

    This is a self paced course, which you can take any time at your convenience over the 6 months after your purchase.

  • How many hours per week should I dedicate to complete the course?

    If you can put between 6 to 8 hours a week, you should be able to finish the course in 4 to 6 weeks.

  • Do I need to install any software before starting the course ?

    You will get information about all installations as part of the course.

  • What is the refund policy?

    The fee for this course is non-refundable.

  • Do I need to take the modules in a specific order?

    We would highly recommend taking the course in the order in which it has been designed to gain the maximum knowledge from it.

  • Do I get a certificate upon completion of the course?

    Yes, you will be given a certificate upon satisfactory completion of the course.

  • What is the fee for this course?

    Fee for this course is INR 7,999

  • How long I can access the course?

    You will be able to access the course material for six months since the start of the course.

Support for Introduction to Data Science Course

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