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Data Science With Python
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Data Science With Python

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Data Science With Python

12 Weeks
Expert
0 lessons
0 quizzes
3 students

Data Science With Python

 

1. Introduction to Statistics

  • Descriptive vs. Inferential Statistics
  • Types of data
  • Measures of central tendency and dispersion
  • Hypothesis & inferences
  • Hypothesis Testing
  • Confidence Interval
  • Central Limit Theorem
  • Probability and Probability Distributions
  • Probability Theory
  • Conditional Probability
  • Data Distribution
  • Distribution Functions
  • Normal Distribution
  • Binomial Distribution

 

2. Python for Data Science

i. An Introduction to Python

  • Python environment Setup/shell
  • Installing Anaconda
  • Understanding the Jupyter Notebook
  • Python Identifiers, Keywords
  • Discussion about installed modules and packages
  • Conditional Statement, Loops, and File Handling
  • Python Data Types and Variable

 

ii. Condition and Loops in Python

  • Decorators
  • Python Modules & Packages
  • Python Files and Directories manipulations
  • Use various files and directory functions for OS operations

 

iii. Python Core Objects and Functions

  • Built-in modules (Library Functions)
  • Numeric and Math Module
  • String/List/Dictionaries/Tuple
  • Complex Data Structures in Python
  • Python built-in function
  • Python user-defined functions

 

iv. Introduction to NumPy

  • Array Operations
  • Arrays Functions
  • Array Mathematics
  • Array Manipulation
  • Array I/O
  • Importing Files with Numpy

 

v. Data Manipulation with Pandas

  • Data Frames
  • I/O
  • Selection in DFs
  • Retrieving in DFs
  • Applying Functions
  • Reshaping the DFs – Pivot
  • Combining DFs
  • Merge
  • Join
  • Data Alignment

 

vi. SciPy

  • Matrices Operations
  • Create matrices
  • Inverse, Transpose, Trace, Norms, Rank, etc
  • Matrices Decomposition
  • Eigen Values & vectors
  • SVDs

 

vii. Visualization with Seaborn

  • Seaborn Installation
  • Introduction to Seaborn
  • Basics of Plotting
  • Plots Generation
  • Visualizing the Distribution of a Dataset
  • Selection color palettes

 

viii. Visualization with Matplotlib

  • Matplotlib Installation
  • Matplotlib Basic Plots & its Containers
  • Matplotlib components and properties
  • Pylab & Pyplot
  • Scatter plots
  • 2D Plots
  • Histograms
  • Bar Graphs
  • Pie Charts
  • Box Plots
  • Customization
  • Store Plots

 

ix. SciKit Learn

  • Basics
  • Data Loading
  • Train/Test Data generation
  • Preprocessing
  • Generate Model
  • Evaluate Models

 

x. Descriptive Statistics

  • Data understanding
  • Observations, variables, and data matrices
  • Types of variables
  • Measures of Central Tendency
  • Arithmetic Mean / Average
  • Merits & Demerits of Arithmetic Mean and Mode
  • Merits & Demerits of Mode and Median
  • Merits & Demerits of Median Variance

 

xi. Probability Basics

  • Notation and Terminology
  • Unions and Intersections
  • Conditional Probability and Independence

 

xii. Probability Distributions

  • Random Variable
  • Probability Distributions
  • Probability Mass Function
  • Parameters vs. Statistics
  • Binomial Distribution
  • Poisson Distribution
  • Normal Distribution
  • Standard Normal Distribution
  • Central Limit Theorem
  • Cumulative Distribution function

 

xiii. Tests of Hypothesis

  • Large Sample Test
  • Small Sample Test
  • One Sample: Testing Population Mean
  • Hypothesis in One Sample z-test
  • Two Sample: Testing Population Mean
  • One Sample t-test – Two Sample t-test
  • Paired t-test
  • Hypothesis in Paired Samples t-test
  • Chi-Square test

 

Introduction to Machine Learning

 

1. Exploratory Data Analysis

  • Data Exploration
  • Missing Value Handling
  • Outliers Handling

 

2. Feature Engineering

  • Feature Selection
  • Importance of Feature Selection in Machine Learning
  • Filter Methods
  • Wrapper Methods
  • Embedded Methods

3. Machine Learning: Supervised Algorithms Classification

4. Logistic Regression

5. Naïve Bays Algorithm

6. K-Nearest Neighbor Algorithm

7. Decision Trees

  • SingleTree
  • Random Forest

 

8. Support Vector Machines

9. Model Ensemble

10. Model Evaluation and performance

  • K-Fold Cross Validation
  • ROC, AUC, etc.

 

11. Hyperparameter tuning

  • Regression
  • classification

 

12. Machine Learning: Regression

  • Simple Linear Regression
  • Multiple Linear Regression
  • Decision Tree and Random Forest Regression

 

13. Machine Learning: Unsupervised Learning Algorithms

  • Similarity Measures
  • Cluster Analysis and Similarity Measures

 

14. Ensemble algorithms

  • Bagging
  • Boosting
  • Voting
  • Stacking
  • K-means Clustering
  • Hierarchical Clustering
  • Principal Components Analysis
  • Association Rules Mining & Market Basket Analysis

 

15. Recommendation Systems

  • collaborative filtering model
  • content-based filtering model.
  • Hybrid collaborative system.

 

Introduction to Data Visualization and the Power of Tableau

 

1. Introduction to Data Visualization and the Power of Tableau

  • Architecture of Tableau
  • Product Components
  • Working with Metadata and Data Blending
  • Data Connectors
  • Data Model
  • File Types
  • Dimensions & Measures
  • Data Source Filters
  • Creation of Sets

 

2. Charts

  • Scatter Plot
  • Gantt Chart
  • Funnel Chart
  • Waterfall Chart
  • Working with Filters
  • Organizing Data and Visual Analytics
  • Working with Calculations and Expressions
  • Working with Parameters
  • Charts and Graphs
  • Dashboards and Stories

 

 

 

 

 

 

 

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