uCertify

Data Science Labs

(DATA-SCI.AA1)/ISBN:978-1-64459-466-7

This course includes
Lessons
Hands-On Labs
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 |

Hands-On Labs

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

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Here's what you will learn

Download Course Outline

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