Introduction to Data Science Certified Course for Beginners

1

Introduction to Data Science
- Getting Started
- Knowing Each Other
- Data Science Overview FREE PREVIEW
- Exercise-1
- Terminologies in Data Science FREE PREVIEW
- Exercise-2
- Applications of Data Science
- Exercise-3
- Instructor's Introduction
- Overview of the course
- Knowing Each other
2

Setting Up the Systems
- Installation steps for Windows
- Installation steps for Linux
- Installation steps for Mac
3

Introduction to Python
- Introduction to Python
- Introduction to Jupyter Notebook
4

Variables and Data Types
- Introduction to Variables
- Implementing Variables in Python
- Quiz: Variables and Data Types
5

Operators
- Introduction to Operators
- Implementing Operators in Python
- Quiz: Operators
6

Conditional Statements
- Introduction to Conditional Statements
- Implementing Conditional Statements in Python
- Quiz: Conditional Statements
7

Looping Constructs
- Introduction to Looping Constructs
- Implementing Loops in Python
- Quiz: Loops in Python
- Break, Continue and Pass Statements
- Quiz: Break, Continue and Pass Statement
8

Data Structures
- Introduction to Data Structures
- List and Tuple
- Implementing List in Pyhton
- Quiz: Lists
- List - Project in Python
- Implementing Tuple in Python
- Quiz: Tuple
- Introduction to Sets
- Implementing Sets in Python
- Quiz: Sets
- Introduction to Dictionary
- Implementing Dictionary in Python
- Quiz: Dictionary
- Practice: Data Structure
9

String Manipulation
- Introduction to String Manipulation
- Quiz: String Manipulation
10

Functions
- Introduction to Functions
- Implementing Functions in Python
- Quiz: Functions
- Lambda Expression
- Quiz: Lambda Expressions
- Recursion
- Implementing Recursion in Python
- Quiz: Recursion
11

Modules, Packages and Standard Libraries
- Introduction to Modules
- Modules: Intuition
- Introduction to Packages
- Standard Libraries in Python
- User Defined Libraries in Python
- Quiz: Modules, Packages and Standard Libraries
12

Handling Text Files in Python
- Handling Text Files in Python
- Quiz: Handling Text Files
- Practice: Handling Text Files
13

Introduction to Python Libraries for Data Science
- Important Libraries for Data Science
- Quiz: Important Libraries for Data Science
14

Python Libraries for Data Science
- Basics of Numpy in Python
- Basics of Scipy in Python
- Basics of Pandas in Python
- Basics of Matplotlib in Python
- Basics of Scikit-Learn in Python
- Basics of Statsmodels in Python
15

Reading Data Files in Python
- Reading Data in Python
- Reading CSV files in Python
- Reading Big CSV Files in Python
- Quiz: Reading CSV files in Python
- Reading Excel & Spreadsheet files in Python
- Quiz: Reading Excel & Spreadsheet files in Python
- Reading JSON files in Python
- Quiz: Reading JSON files in Python
- Practice: Reading Data Files in Python
16

Preprocessing, Subsetting and Modifying Pandas DataFrames
- Subsetting and Modifying Data in Python
- Overview of Subsetting in Pandas I
- Overview of Subsetting in Pandas II
- Subsetting based on Position
- Subsetting based on Label
- Subsetting based on Value
- Quiz: Subsetting DataFrames
- Modifying data in Pandas
- Quiz: Modifying DataFrames
- Practice: Subsetting and Modifying Pandas Dataframes
17

Sorting and Aggregating Data in Pandas
- Preprocessing, Sorting and Aggregating Data
- Sorting the DataFrame
- Quiz: Sorting DataFrame
- Concatenating DataFrames in Pandas
- Concept of SQL-Like Joins in Pandas
- Implementing SQL-Like Joins in Pandas
- Quiz: Joins in Pandas
- Aggregating and Summarizing DataFrames
- Preprocessing TimeSeries Data
- Quiz: Preprocessing TimeSeries Data
- Practice: Sorting and Aggregating Data in Pandas
18

Visualizing Patterns and Trends in Data
- Visualizing Trends & Pattern in Data
- Basics of Matplotlib
- Data Visualization with Matplotlib
- Quiz: Matplotlib
- Basics of Seaborn
- Data Visualization with Seaborn
- Quiz: Seaborn
- Practice: Visualizing Patterns and Trends in Data
19

Machine Learning Lifecycle
- 7 Steps of Machine Learning Lifecycle
- Introduction to Predictive Modeling
20

Problem statement and Hypothesis Generation
- Defining the Problem statement
- Introduction to Hypothesis Generation
- Performing Hypothesis generation
- Quiz - Performing Hypothesis generation
- List of hypothesis
- Data Collection/Extraction
- Quiz - Data Collection/Extraction
21

Importance of Stats and EDA
- Introduction to Exploratory Data Analysis & Data Insights
- Quiz - Introduction to Exploratory Data Analysis & Data Insights
- Role of Statistics in EDA
- Descriptive Statistics
- Inferential Statistics
- Quiz - Descriptive and Inferential Statistics
22

Understanding Data
- Introduction to dataset
- Quiz - Introduction to dataset
- Reading data files into python
- Quiz - Reading data files into python
- Different Variable Datatypes
- Variable Identification
- Quiz - Variable Identification
23

Basics of Probability
- Probability for Data Science
- Quiz - Probability for Data Science
- Basic Concepts of Probability
- Quiz - Basic Concepts of Probability
- Axioms of Probability
- Quiz - Axioms of Probability
- Conditional Probability
- Quiz - Conditional Probability
24

Exploring Continuous Variable
- Data range for continuous variables
- Central Tendencies for continuous variables
- Spread of the data
- Central Tendencies and Spread of the data: Implementation
- Quiz: Central Tendencies and Spread of data
- KDE plots for continuous variable
- KDE plots : Implementation
- Overview of Distributions for Continuous Variables
- Normal Distribution
- Normality Check
- Skewed Distribution
- Distributions for continuous variable
- Quiz: Distribution of Continuous variables
- Skewness and Kurtosis
- Approaching Univariate Analysis
- Approaching Univariate Analysis: Numerical Variables
- Quiz: Univariate analysis for Continuous variables
25

Exploring Categorical Variables
- Central Tendencies for categorical variables
- Understanding Discrete Distributions
- Discrete Distributions Demonstration
- Performing EDA on Catagorical Variables
- Quiz: Univariate Analysis for Categorical Variables
26

Missing Values and Outliers
- Dealing with Missing values
- Understanding Outliers
- Identifying Outliers in data
- Identifying Outliers in data: Implementation
- Quiz: Identifying Outliers in datasets
- Quiz: Outlier treatment
27

Central Limit Theorem
- Important Terminologies
- Central Limit Theorem
- CLT: Implementation
- Quiz: Central Limit Theorem
- Confidence Interval and Margin of error
28

Exploring Continuous - Continuous Variables
- Introduction to Bivariate Analysis
- Covariance
- Pearson Correlation
- Spearman's Correlation & Kendall's Tau
- Correlation versus Causation
- Tabular and Graphical Methods
- Performing Bivariate Analysis on Continuous - Continuous variables
- Quiz: Continuous-Continuous Variables
29

Continuos Categorical
- Tabular and Graphical Methods
- Introduction to hypothesis Testing
- P-Value
- One Sample z-test
- Two Sampled z-test
- Quiz: Hypothesis Testing and Z scores
- T-Test
- T-Test vs Z-Test
- Quiz: T tests
- Performing Bivariate Analysis on Catagorical - Continuous variables
30

Categorical Categorical Variables
- Tabular and Graphical Methods
- Chi-Squared Test
- Quiz: Chi squared tests
- Bivariate Analysis for Categorical Categorical Variables
31

Multivariate Analysis
- Multivariate Analysis
- Multivariate Analysis Implementation
32

Assignments
- Understanding the NYC Taxi Trip Duration Problem
- Practice: EDA
33

Build your first Predictive Model
- Introduction and Overview FREE PREVIEW
- Quiz: Introduction and Overview FREE PREVIEW
- Creating the Dataset FREE PREVIEW
- Quiz: Creating the dataset FREE PREVIEW
- Problem Statement: Regression FREE PREVIEW
- Quiz: Problem Statement - Regression
- Benchmark Model: Regression Implementation
- Quiz: Benchmark Model - Regression Implementation
- Problem Statement: Classification
- Quiz: Problem Statement - Classification
- Benchmark Model: Classification Implementation
- Quiz: Benchmark - Classification Implementation
34

Evaluation Metrics
- Introduction to Evaluation Metrics
- Quiz: Introduction to Evaluation Metrics
- Confusion Matrix
- Quiz: Confusion Matrix
- Accuracy
- Quiz: Accuracy
- Alternatives of Accuracy
- Quiz: Alternatives of Accuracy
- Precision and Recall
- Quiz: Precision and Recall
- Thresholding
- Quiz: Thresholding
- AUC-ROC
- Quiz: AUC-ROC
- Log loss
- Quiz: Log loss
- Evaluation Metrics for Regression
- Quiz: Evaluation Metrics for Regression
- R2 and Adjusted R2
- Quiz: R2 and Adjusted R2
35

Data Preprocesssing
- Dealing with Missing Values in the Data
- Replacing Missing Values
- Imputing Missing Values in data
- Working with Categorical Variables
- Working with Outliers
- Preprocessing Data for Model Building
36

Build your First ML model
- Introduction to k-Nearest Neighbours FREE PREVIEW
- Quiz: Introduction to k-Nearest Neighbours FREE PREVIEW
- Building a kNN model
- Quiz: Building a kNN model
- Selecting right value of k
- Quiz: Selecting right value of k
- How to calculate the distance?
- Quiz: How to calculate the distance
- Issue with distance based algorithms
- Quiz: Issue with distance based algorithms
- Introduction to sklearn
- Implementing k-Nearest Neighbours algorithm
- Quiz: Implementing k-Nearest Neighbours algorithm
37

Selecting Right model (Overfit/Underfit, validation, bias-variance)
- Introduction to Overfitting and Underfitting Models
- Quiz: Introduction to Overfitting and Underfitting Models
- Visualizing overfitting and underfitting using knn
- Quiz: Visualizing overfitting and underfitting using knn
- Selecting the Right Model
- What is Validation?
- Quiz: What is Validation?
- Understanding Hold-Out Validation
- Quiz: Understanding Hold-Out Validation
- Implementing Hold-Out Validation
- Quiz: Implementing Hold-Out Validation
- Understanding k-fold Cross Validation
- Quiz: Understanding k-fold Cross Validation
- Implementing k-fold Cross Validation
- Quiz: Implementing k-fold Cross Validation
- Bias Variance Tradeoff
- Quiz: Bias Variance Tradeoff
38

Linear Models
- Introduction to Linear Models
- Understanding Cost function
- Quiz: Understanding Cost function
- Understanding Gradient descent (Intuition)
- Maths behind gradient descent
- Convexity of cost function
- Quiz: Gradient Descent
- Assumptions of Linear Regression
- Preparing Data for Model Building
- Implementing Linear Regression
- Generalized Linear Models
- Quiz: Generalized Linear Models
- Introduction to Logistic Regression
- Odds Ratio
- Implementing Logistic Regression
- Quiz: Logistic Regression
- Multiclass using Logistic Regression
- Quiz: Multi-Class Logistic Regression
- Challenges with Linear Regression
- Introduction to Regularisation
- Quiz: Introduction to Regularization
- Implementing Regularisation
- Coefficient estimate for ridge and lasso (Optional)
39

Project
- Problem Statement - Customer Churn Prediction
- Predicting whether a customer will churn or not
40

Practice
- Practice: NYC taxi trip duration prediction
41

Introduction to Dimensionality Reduction
- Introduction to Dimensionality Reduction
- Quiz: Introduction to Dimensionality Reduction
- Common Dimensionality Reduction Techniques
- Quiz: Common Dimensionality Reduction Techniques
- Missing Value Ratio
- Missing Value Ratio Implementation
- Quiz: Missing Value Ratio
- Low Variance Filter
- Low Variance Filter Implementation
- Quiz: Low Variance Filter
- High Correlation Filter
- High Correlation Filter Implementation
- Quiz: High Correlation Filter
- Backward Feature Elimination
- Backward Feature Elimination Implementation
- Quiz: Backward Feature Elimination
- Forward Feature Selection
- Forward Feature Selection Implementation
- Quiz: Forward Feature Selection
42

Decision Tree
- Introduction to Decision Trees
- Quiz: Introduction to Decision Trees
- Purity in Decision Trees
- Quiz: Purity in Decision Trees
- Terminologies Related to Decision Trees
- Quiz: Terminologies Related to Decision Trees
- How to Select the Best Split Point in Decision Trees
- Quiz: How to Select the Best Split Point in Decision Trees
- Chi-Square
- Quiz: Chi-Square
- Information Gain
- Quiz: Information Gain
- Reduction in Variance
- Quiz: Reduction in Variance
- Optimizing Performance of Decision Trees
- Quiz: Optimizing Performance of Decision Trees
- Decision Tree Implementation
43

Basics of Feature Engineering
- Introduction to Feature Engineering
- Exercise on Feature Engineering
- Overview of the module
- Feature Transformation
- Quiz: Feature Transformation
- Feature Scaling
- Quiz: Feature Scaling
- Feature Encoding
- Quiz: Feature Encoding
- Combining Sparse classes
- Quiz: Combining Sparse classes
- Feature Generation: Binning
- Feature Interaction
- Quiz: Feature Interaction
- Generating Features: Missing Values
- Frequency Encoding
- Quiz: Frequency Encoding
- Feature Engineering: Date Time Features
- Implementing DateTime Features
- Quiz: Implementing DateTime Features
44

Project: NYC Taxi Trip Prediction
- Exploring the NYC dataset
- Predicting the NYC taxi trip duration (Decision tree)
- Downloads Notebook and DataSets
45

Unsupervised Machine Learning
- Introduction to Clustering
- Quiz: Introduction to Clustering
- Applications of Clustering
- Evaluation Metrics for Clustering
- Quiz: Evaluation Metrics for Clustering
- Understanding K-Means
- K-Means from Scratch Implementation
- Quiz: Understanding K-Means
- Challenges with K-Means
- How to Choose Right k-Value
- K-Means Implementation
- Quiz: K-Means Implementation

I have no programming experience. Would I need to learn Python to learn data science?
Programming is an essential aspect of being a data scientist or a data science professional. And Python is the market leader in this space. Organizations globally are adopting Python as their go-to language, including big tech firms like Spotify, Netflix, Facebook, among others.

Python consistently ranks top in global data science surveys and its widespread popularity will only keep on increasing in the coming years.

Over the years, with strong data science community support, this language has obtained a dedicated library for data analysis and predictive modelling.

And don’t worry! Python is a very easy language to learn and we cover it from scratch in the course. So you don’t need to have any prior programming knowledge to master Python!
Do I need to know statistics before taking this course?
No! Statistics is the backbone of data science and we understand that. We have designed an entire comprehensive module on statistics which we cover in the course.

We will cover both descriptive statistics and inferential statistics in detail, along with how to implement each concept in Python. And once you’ve learned and practiced statistics concepts, we will then jump to data science modelling.
What kind of projects can I take up after this course?
You can take up a variety of data science projects! Since this covers both regression and classification algorithms, like linear regression, logistic regression and decision trees, you’ll be well equipped to apply your data science and Python skills on real world projects.

We recommend you pick up the projects we’ve curated on the DataHack platform. These projects will hone your data science skills and enhance what you have learned in the Introduction to Data Science course.
Can I add the projects covered in this course in my resume?
Of course! Projects are among the first things a hiring manager or recruiter looks for in a data science resume. The more projects you add, the stronger your chance of landing your dream role.

As mentioned above, you can head to the DataHack platform and pick up projects from there. Practice is key in data science!
Will this course help me clear data science interviews?
This course will help you build a solid base for data science. You will learn a new programming language (Python), the backbone of data science (statistics), and core predictive modeling techniques.

As a next step, you should go through our course - Ace Data Science Interviews.