Advanced Data Science Training Course Content

Data Science Overview

• Introduction to Data Science
• Different Sectors using Data Science
• Python vs R
• What is the role of a Data Analyst
• What is the role of a Data
• Scientist
• Opportunities in Data Science

Data Analysis Overview

• Data Analysis Process
• Knowledge Check
• Exploratory Data Analysis
• EDA - Quantitative Technique
• Types of Variables
• Types of Problems

Data Visualization Overview

• Why Visualize everything ?
• Importance of Visualization
• Types of Visualization Strategies
• Color Palettes
• Types of Charts
• When to Use Different Charts
• Sub Plots
• Plotting with Pandas
• Matplotlib
• Seaborn
• Plotly - Advanced Data Visualization
• Scientific Data Visualization
• Driving Insights from Visualizations

Python Programming Language

• History and Overview
• Applications and Use Cases
• Syntax, Comments, Variables
• Data Types, Numbers, Casting
• Strings, Booleans, Operators
• Lists, Tuples
• Sets, Dictionaries
• Loops and Conditionals
• Lambdas and Arrays
• Classes and Objects
• Inheritance
• Iterators
• Scope and Modules
• Dates and Json
• Regular Expressions
• File Handling
• Python for MongoDB
• Python for MySQL
• Quizzes

Numpy

• Creating Numpy Arrays
• Mathematical Functions
• Subset, Slice, and Index
• Manipulating Numpy Arrays
• Join, Split, Resize etc.
• Transpose, Reshape
• Visualizing Numpy Arrays
• Data Analysis with Numpy
• Numpy Functions

Pandas

• Introduction to DataFrame
• DataFrame Operations
• Pandas Functions
• Plotting with Pandas
• Advanced Pandas Trick
• Exploratory Data Analysis
• Data Ingestion and Inspection

Statistical Analysis

• Introduction to Statistics
• Mean, Mode, Standard Deviation
• Scales and Measures
• Basic Terms and Terminologies
• Use Cases and Problems
• Summarizing Distribution
• Graphing Distribution
• Univariate Data
• Bivariate Data
• Multivariate Data
• Normal Distributions
• Probability
• Sampling Distributions
• Advanced Graphs
• Estimation
• Hypothesis Testing
• A/B Testing
• Analysis of Variance
• Standardization
• Normalization
• Box Cox Transformation
• Cosine Similarity

Advanced Statistics

• Bernoulli and Binomial Distribution
• Chebyshev’s Inequality
• Correlation vs Causation
• Interquartile Range
• Kernel Density Estimation
• QQ Plot
• Skewness
• Resampling and Permutation test

Supervised Machine Learning

• Introduction
• Types of Supervised Techniques
• Regression Analysis
  • Types of Regression Algorithms
  • Linear Regression
  • Logistic Regression
  • Lasso Regression
  • Ridge Regression
  • Elastic Net Regression
  • Metrics for Regression Analysis
• Classification Analysis
  • Decision Trees
  • Random Forest
  • Support Vector Machines
  • K Nearest Neighbors
  • Naive Bayes Theorem
  • AdaBoost Classifier
  • Metrics for Classification

PROJECT 1: Breast Cancer Dataset

PROJECT 2: Employee Dataset

PROJECT 3: Black Friday Dataset

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