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Data Science Labs

Start your data science journey with Python. Gain hands-on experience with NumPy, Pandas, Matplotlib, Seaborn, and more.

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

Data Science Labs is a comprehensive online training course that focuses on Python for data analysis, manipulation and visualization. Discover the fundamentals of Python programming, explore data structures and master data manipulation techniques. Gain practical skills in data analysis using Pandas, perform numerical calculations, create clear and insightful visualizations, and develop predictive models using regression. Additionally, this data science training course has been fully equipped with hands-on Labs where you can practice and solidify your learnings.

Skills You’ll Get

  • Create, manipulate, and analyze data frames with Pandas 
  • Ability to use NumPy for arrays, indexing, slicing, and mathematical operations
  • Create informative visuals with Matplotlib, Seaborn, Plotly, and Bokeh
  • Build and evaluate regression models
  • Knowledge of logistic regression for classification tasks
  • Exploratory Data Analysis (EDA) for hypothesis testing, data visualization, and identifying patterns
  • Extract, transform, and load data using ETL techniques
  • Conduct in-depth exploratory data analysis

Download Course Outline

1

Pandas

  • About DataFrames
  • Creating DataFrames
  • Interacting with DataFrame Data
  • Manipulating DataFrames
  • Manipulating Data
  • Interactive Display
  • Summary
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
3

Visualization Libraries

  • matplotlib
  • Seaborn
  • Plotly
  • Bokeh
  • Other Visualization Libraries
  • Summary
4

Extracting, Transforming, and Loading Data

  • Topic A: Extract Data
  • Topic B: Transform Data
  • Topic C: Load Data
  • Summary
5

Developing Regression Models

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

1

Pandas

  • Creating a Series from a List Using pandas
  • Creating a Series from a Dictionary Using pandas
  • Using the read_csv() Function
2

NumPy

  • Creating a One-Dimensional Array Using numpy
  • Creating a Multi-Dimensional Array Using numpy
3

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
4

Extracting, Transforming, and Loading Data

  • Performing Data Cleaning
  • Handling the Missing Values
5

Developing Regression Models

  • Performing Linear Regression on the Salary Dataset
6

Logistic Regression

  • Performing Logistic Regression
7

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

Any questions?
Check out the FAQs

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It is an online training and skill development course that teaches the use of Python for data analysis. It is an elaborate course that talks about Pandas, NumPy, data extraction, and regression.

Python is a popular language for data analysis. It has a clean syntax which makes it easy to learn and understand, even for those without a programming background. Plus, Python's rich ecosystem, versatility, community support, and open-source nature make it an ideal choice.

Yes. Although it is a beginner-friendly course that covers all the fundamentals, it is recommended that the student should have basic programming skills, an understanding of mathematical and statistical concepts with a keen interest in data and willingness to explore.

Yes, you’ll be awarded a certification of completion after finishing the course.

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