Data Science Labs

Immerse yourself in the world of data science with the Data Science Labs course. Through interactive lessons and hands-on labs, you’ll gain practical experience in performing various data science tasks, including creating and manipulating DataFrames using Pandas, working with NumPy arrays, and exploring essential third-party libraries. Get ready to dive into real-world data challenges and develop the skills needed to excel in the dynamic field of data science.

Lessons

7+ Lessons |

Hand on lab

25+ LiveLab | 25+ Video tutorials | 30+ Minutes

Lessons 1: Pandas

• About DataFrames
• Creating DataFrames
• Interacting with DataFrame Data
• Manipulating DataFrames
• Manipulating Data
• Interactive Display
• Summary

Lessons 2: NumPy

• Installing and Importing NumPy
• Creating Arrays
• Indexing and Slicing
• Element-by-Element Operations
• Filtering Values
• Views Versus Copies
• Some Array Methods
• Broadcasting
• NumPy Math
• Summary

Lessons 3: Visualization Libraries

• matplotlib
• Seaborn
• Plotly
• Bokeh
• Other Visualization Libraries
• Summary

Lessons 4: Extracting, Transforming, and Loading Data

• Topic A: Extract Data
• Topic B: Transform Data
• Topic C: Load Data
• Summary

Lessons 5: Developing Regression Models

• Topic A: Train and Tune Regression Models
• Topic B: Evaluate Regression Models
• Summary

Lessons 6: Logistic Regression

• Simple Example of Logistic Regression
• Maximum Likelihood Estimation
• Interpreting Logistic Regression Output
• Inference: Are the Predictors Significant?
• Odds Ratio and Relative Risk
• Interpreting Logistic Regression for a Dichotomous Predictor
• Interpreting Logistic Regression for a Polychotomous Predictor
• Interpreting Logistic Regression for a Continuous Predictor
• Assumption of Linearity
• Zero-Cell Problem
• Multiple Logistic Regression
• Introducing Higher Order Terms to Handle Nonlinearity
• Validating the Logistic Regression Model
• WEKA: Hands-On Analysis Using Logistic Regression

Lessons 7: Exploratory Data Analysis

• Hypothesis Testing Versus Exploratory Data Analysis
• Getting to Know The Data Set
• Exploring Categorical Variables
• Exploring Numeric Variables
• Exploring Multivariate Relationships
• Selecting Interesting Subsets of the Data for Further Investigation
• Using EDA to Uncover Anomalous Fields
• Binning Based on Predictive Value
• Deriving New Variables: Flag Variables
• Deriving New Variables: Numerical Variables
• Using EDA to Investigate Correlated Predictor Variables
• Summary of Our EDA

Hands-on LAB Activities

Pandas

• Creating a Series from a List Using pandas
• Creating a Series from a Dictionary Using pandas
• Using the read_csv() Function

NumPy

• Creating a One-Dimensional Array Using numpy
• Creating a Multi-Dimensional Array Using numpy

Visualization Libraries

• Creating a Bar Plot Using matplotlib
• Creating a Line Plot Using matplotlib
• Creating a Scatter Plot Using matplotlib
• Creating a Pie Chart Using matplotlib
• Creating a Confusion Matrix
• Creating a Line Plot Using seaborn
• Adding Animation to a Choropleth Map Using Plotly Express
• Creating Different Shapes Using bokeh
• Creating a Linked Scatter Plot Using altair

Extracting, Transforming, and Loading Data

• Performing Data Cleaning
• Handling the Missing Values

Developing Regression Models

• Performing Linear Regression on the Salary Dataset

Logistic Regression

• Performing Logistic Regression

Exploratory Data Analysis

• Analyzing Students’ Performance
• Performing Data Analysis on Movies and TV Shows on Netflix
• Performing Data Analysis on Movies and TV Shows on Amazon Prime
• Comparing Movies and TV Shows Data on Amazon Prime and Netflix
• Performing Data Analysis on Google Play Store Data
• Performing Data Analysis on Video Game Sales Data
• Performing Exploratory Data Analysis