• 2 Hours

  • 4.8/5

  • Beginner

About the course

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 2023. 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 2023 for data scientists including storytelling, model deployment, and much more along with exercises and assignments.

Course curriculum

  • 1
    Overview of the Learning Path 2024
    • Overview of Learning Path
    • Month-on-Month Plan
    • Your Personalized Learning Path for Data Science
    • AI&ML Blackbelt Plus Program (Sponsored)
  • 2
    January 2024: Data Science Toolkit
    • Plan for January 2024
    • Understanding Machine Learning and its impact
    • Job of Data Scientist
    • Exercise
    • Overview of the Course
    • A brief introduction to Python
    • Introduction to Python Test
    • Installing Python
    • Theory of Operators
    • Exercise
    • Understanding Operators in Python
    • Operators Test
    • Understanding variables and data types
    • Variable Test
    • Variables and Data Types in Python
    • Understanding Conditional Statements
    • Exercise
    • Implementing Conditional Statements in Python
    • Conditional Statements test
    • Understanding Looping Constructs
    • Exercise
    • Implementing Looping Constructs in Python
    • Looping Constructs test
    • Understanding Functions
    • Implementing Functions in Python
    • Functions test
    • A brief introduction to data structure
    • Data Structure test
    • Understanding the concept of Lists
    • 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
    • Sorting Dataframes
    • Merging Dataframes
    • Quiz: Sorting and Merging dataframes
    • Apply function
    • Aggregating data
    • Quiz: Apply function and Aggregating data
    • Basics of Matplotlib
    • Data Visualization using Matplotlib
    • Quiz: Matplotlib
    • Basics of Seaborn
    • Data Visualization using Seaborn
    • Quiz: Seaborn
    • Regular Expressions
    • Understanding Regular Expressions
    • Quiz: Regular Expressions
    • Regular Expressions in Python
    • Quiz: Regular Expressions in Python
    • Cheatsheet for Python
    • Instructions
    • Quiz
    • Python Coding Challenge
  • 3
    February 2024: Data Visualization
    • The Power of Visualization FREE PREVIEW
    • What is Data Visualization and Why Should we Use it FREE PREVIEW
    • Exercise - Definition of Data Visualization
    • Hans Rosling - 200 Countries 200 Years 4 Minutes FREE PREVIEW
    • 4 Key Elements of Effective Data Visualizations FREE PREVIEW
    • Why Tableau is a Powerful Tool for Professionals
    • What We Will Cover in this Course FREE PREVIEW
    • Compare Tableau Against Power BI and Qlik
    • The Tableau Range of Products
    • The 5 Tableau Products you should Know
    • Installing Tableau Desktop on your System
    • Installing Tableau Public on your System
    • Difference Between Tableau Server and Tableau Online
    • Navigating the Tableau Interface (Part 1)
    • Navigating the Tableau Interface (Part 2)
    • Connecting to Data Sources in Tableau
    • Understanding the Problem Statement
    • Download the Superstore Dataset
    • Loading the Dataset and Getting Familiar with the Variables
    • Build your First Visualization in Tableau!
    • Hands-On with Labels and Formatting
    • Playing Around with Colors
    • Using Filters to Build a Pivot Structure in Tableau
    • Exporting your Tableau Worksheet
    • The Different Chart Types in Tableau
    • Line Charts - Working with Time Series Data
    • Building Line Charts in Tableau
    • Exercise - Sales of Each Category Month-by-Month
    • Generating Map Visualizations for Geospatial Analysis
    • Map Visualizations in Tableau
    • Exercise - Sales by City Analysis
    • Bar Charts, Histograms, Scatter Plots, Bubble Charts, Pie Charts
    • Dual Axis Charts in Tableau
    • Date Dual Axis Charts in Tableau
    • What are Calculated Fields?
    • Feature Engineering in Tableau - Average Shipping Time
    • Exercise - Number of Orders per State
    • Calculating the Average Order Value
    • Average Order Value for Product Sub-Categories
    • What are Parameters in Tableau?
    • Using Parameters to find Top N Customers
    • Using Parameters to Analyze Superstore's Variable Values
    • Joins and their Different Types in Tableau
    • Performing Data Joining in Tableau
    • What is Blending? How is it Different from Joins?
    • Blending Data in Tableau
    • Download the Coffee Chain Dataset
    • Introduction to Dashboards and their Use Cases
    • Reading Material - Dashboards in Tableau
    • Designing your First Dashboard in Tableau
    • Using Parameters to Create Dynamic Dashboards
    • How to Upload your Work to the Tableau Public Gallery
    • Designing the Blueprint for a Multi-Dashboard View to Analyze Sales
    • Building Multiple Interlinked Dashboards in Tableau for our Business
    • The Art of Storytelling
    • 3-Step Storytelling Framework
    • Sketching the Story Blueprint
    • Profits by Region Analysis using Storyboard in Tableau
    • Capstone Project: Sales and Profit by Segment using Storyboards in Tableau
    • Getting started with SQL
    • Introduction FREE PREVIEW
    • Why do we need databases? FREE PREVIEW
    • What is a database? FREE PREVIEW
    • Some properties of a Good Database FREE PREVIEW
    • Types of Databases
    • How data is Stored in Relational Databases
    • How data is stored in NoSQL databases
    • Companies using MySQL FREE PREVIEW
    • Exercise 1
    • Introduction
    • Architecture: Client and Server
    • MySQL Distributions
    • Local Installation on Mac
    • Local Installation on Linux
    • Local Installation on Windows
    • Licensing
    • Accessing a remote MySQL server
    • Graphical user interfaces
    • Exercise 2
    • SQL - Installation Guide
    • Introduction
    • What exactly is SQL?
    • History of SQL
    • Connecting to MySQL
    • Types of Commands - DDL (Creation/ Deletion/ Updating of Schema
    • Types of Commands - DML (Manipulating data in tables)
    • Types of Commands - DCL (Managing Access control)
    • Exploring databases
    • Creating tables
    • Inserting data in tables
    • SELECT Statement - Introduction
    • Datatypes in MySQL
    • NULL vs NOT NULL
    • Exercise 3
    • Introduction
    • Update command – Concept
    • Update command – Example
    • Delete command – Concept
    • Delete command – Example
    • Describe command – Concept
    • Describe command – Example
    • Alter command – Concept and Example
    • Exercise 4
    • Introduction
    • Importing data from CSV to MySQL
    • Exporting data from MySQL to CSV
    • Backing up databases
    • Restoring databases
    • Exercise 5
    • Importing and Exporting Datasets - Troubleshooting Guide
    • Introduction
    • Counting Rows and Items
    • Aggregation Functions – SUM, AVG, STDDEV
    • Extreme Values Identification – MIN, MAX
    • Slicing data
    • Limiting data
    • Sorting data
    • Filtering Patterns
    • Groupings, Rolling up data and Filtering in Groups
    • Exercise 6
    • Introduction
    • Data Eyeballing
    • Data Dictionary
    • Questions we need answers of
    • Analyzing data and creating table structure
    • Loading data to our MySQL table
    • Data Analysis – Simple Queries
    • Data Analysis – Advanced Queries
    • FIFA19 Players dataset (cleaned) for this Project
    • Introduction
    • The need for joins
    • Different type of joins
    • The Left Join - Concept
    • The Left Join – Practical Example
    • The Inner Join
    • The Cross Join
    • The Right Join
    • The Self Join
    • Exercise
    • Introduction
    • Introduction to Indexing
    • How indexing works (basics)
    • Relationships
    • Types of Relationships
    • Table Constraints – PRIMARY KEY, FOREIGN KEY, UNIQUENESS and AUTO INCREMENT
    • Exercise
    • String functions - CONCAT
    • String functions – Case Conversion
    • String functions – Trimming Strings
    • String functions – Extracting Substrings
    • Date/ Time functions – Current date and time
    • Date/ Time functions – Extracting date and time from field
    • Date/ Time functions – Formatting date and time as Strings
    • Numeric functions
    • SQL CheatSheet
    • Exercise
    • Introduction
    • Setting up a virtual environment
    • Installing the required packages
    • Connecting to MySQL
    • Connecting to database table and pulling data
    • Querying the database- INSERT
    • Querying the database- DELETE
    • Querying the database- SEARCH
    • Querying the database- INDEXING
    • Notes and Resources
    • Subscribe to Data Science Newsletter and Podcast
  • 4
    March 2024: Data Exploration
    • Overview of Statistics
    • Important applications of Statistics
    • What is Descriptive Statistics?
    • Introduction to Design experiments
    • Introduction to Design experiments-Video
    • Exercise
    • Visualizing Data
    • Visualizing Data
    • Central tendency
    • Exercise
    • Variability
    • Unimodal Distribution of Data
    • Bimodal Distribution of Data
    • Normal distribution – Part 1
    • Normal distribution – Part 2
    • Z-Score
    • Introduction to Probability- An Overview
    • Principal Of Counting
    • Exercise
    • Permutation
    • Exercise
    • Combination
    • Exercise
    • Conditional Probability – Part 1
    • Conditional Probability – Part 2
    • Exercise
    • Binomial Distribution
    • Random variable
    • Expectation and variance
    • Exercise
    • Cheatsheet for Probability
    • Statistics: Inferential-Hypothesis Testing
    • T-test
    • One Way ANOVA
    • Chi-square
    • Cheatsheet on Statistics
    • Exploratory Data Analysis (EDA)- Data Exploration
    • Cheatsheet on EDA
    • Project-1 | Loan Prediction
    • Project-2 | Big Mart Sales
    • Linear Algebra
    • Free Course
  • 5
    April 2024: Basics of Machine Learning and art of storytelling
    • Overview of Machine Learning
    • Understanding Data Science Pipeline
    • Get Familiarised with Command Line (Linux)- Guide
    • Linear Regression
    • Linear Regression-Video
    • Exercise
    • Logistic Regression- Part 1
    • Logistic Regression – Part 2
    • Exercise
    • Decision Tree Algorithm
    • Exercise
    • Naive Bayes
    • Support Vector Machine
    • Regression Project - Big Mart Sales
    • Classification Project - Loan Prediction
    • Introduction to Structured Thinking
    • Commonly Asked Puzzles in Interviews
    • How to solve Guesstimates?
    • Excercise: Strategic Thinking
    • Structured Thinking and Communication Course
  • 6
    May 2024: Advanced Machine Learning
    • Ensemble Learning Basics
    • Ensemble Learning Basics-Video
    • Bagging
    • Boosting
    • Random Forest - Simplified
    • Random Forest - Detailed with implementation
    • Exercise
    • Boosting - Detailed with implementation
    • XGBoost
    • LightGBM
    • CatBoost
    • Exercise
    • Advanced Ensemble Technique - Blending
    • Advanced Ensemble Learning - Stacking
    • Cheatsheet for Machine Learning
    • Image data
    • Text data
    • Audio data
    • Audio data-Video
    • Projects
    • Participating in Competitions
    • Introduction to validation
    • Different Types of Validation Techniques
    • K-fold Cross Validation - Implementation
    • Summary - Validation Techniques
    • Exercise
    • Different methods for finding best hyperparameters of an algorithm
    • Hyperparameter tuning for Random Forest
    • Hyperparameter tuning for GBM
    • Hyperparameter tuning for XGBoost
    • Hyperparameter tuning for LightGBM
    • Bayesian Hyperparameter Optimization
    • Feature Engineering
    • Profile Building
    • Building your Resume
    • Up Level your Data Science Resume Course
    • Ace Data Science Interview Course
  • 7
    June 2024: Other Machine Learning Concepts
    • Basics of Matrix Algebra
    • Matrix Calculus
    • Dimensionality Reduction - Overview
    • Principal Component Analysis (PCA)
    • Singular Value Decomposition (SVD)
    • Singular Value Decomposition (SVD)-Text
    • Unsupervised Learning-K Means and Hierarchical Clustering
    • Clustering - Project
    • Learn Github
    • Introduction to Recommendation Systems
    • Introduction to Recommendation Systems - Video
    • Project: Recommendation System
    • Implementation in Python
    • Introduction to Time Series Forecasting
    • Handling a Non-Stationary Time Series in Python
    • Time Series Modeling using ARIMA
    • Time Series Modeling using Prophet Library
    • Time Series Project
    • Project - Black Friday
  • 8
    July 2024: Introduction to Deep Learning and Computer Vision
    • Setting up the System for Deep Learning
    • Introduction to Deep Learning
    • Build your first Neural Network in Numpy
    • Why are GPUs necessary for Deep Learning?
    • The Evolution and Core Concepts of Deep Learning & Neural Networks
    • An Introduction to Implementing Neural Networks using TensorFlow
    • Introduction to Keras
    • Optimizing Neural Networks using Keras (with Image recognition case study)
    • Understanding Convolutional Neural Networks (CNNs)
    • Build Image Classification Model using Keras
    • Exercise
    • Transfer Learning for Computer Vision
    • Computer Vision Project 1 : Identify the Apparels
    • Computer Vision Project 2: Scene Classification
    • Computer Vision using Deep Learning Course
    • Computer Vision Course (Sponsored)
    • Cheatsheet for Keras
    • Write for Analytics Vidhya's Medium Publication
  • 9
    August 2024: Basics of Natural Language Processing
    • Recurrent Neural Network
    • Long short Term Memory Networks (LSTM)
    • Gated Recurrent Unit (GRU)
    • Useful resources-GRU
    • Text Preprocessing
    • Text Cleaning
    • Text Classification
    • Natural Language Processing (NLP) Using Python Course
  • 10
    September 2024: Model Deployment
    • How to Deploy Machine Learning Models using Flask
    • Tutorial to deploy Machine Learning models in Production as APIs
    • Deploying machine learning models using Streamlit – An introductory guide to Model Deployment
    • An Ode to Model Deployment using Streamlit – Open Sourcing “Typing Tutor for Programmers”
  • 11
    October 2024: Practice and Projects
    • Building a Portfolio with Projects

Certificate of Completion

Unlock a lifetime-valid certificate from Analytics Vidhya upon completing the course—your achievement is forever recognized!
Certificate of Completion

Instructor

Kunal Jain, Founder & CEO, Analytics Vidhya

Kunal has 15+ years of experience in the field of Data Science and is the founder and CEO of Analytics Vidhya- the world's 2nd largest Data Science community.
Instructor

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 2023: 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 2023.

  • 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.