Data Science with Python Training Courses

Course Introduction
1.1 Course Introduction
1.2 Demo Jupyter Lab Walk Through

Introduction to Data Science
2.1 Learning Objectives
2.2 Data Science Methodology
2.3 From Business Understanding to Analytic Approach
2.4 From Requirements to Collection
2.5 From Understanding to Preparation
2.6 From Modeling to Evaluation
2.7 From Deployment to Feedback
2.8 Key Takeaways

Python Libraries for Data Science
3.1 Learning Objectives
3.2 Python Libraries for Data Science
3.3 Import Library info Python Program
3.4 Numpy
3.5 Demo Numpy
3.6 Fundamentals of Numpy
3.7 Numpy Array Shapes and axes Part A
3.8 Numpy Array Shapes and axes Part B
3.9 Arithmetic Operations
3.10 Conditional Statements in Python
3.11 Common Mathematical and Statistical Functions in NumPy
3.12 Indexing and Slicing in Python Part A
3.13 ndexing and Slicing in Python Part B
3.14 Introduction to Pandas
3.15 Introduction to Pandas Series
3.16 Querying a Series
3.17 Pandas Dataframe
3.18 Introduction to Pandas Panel
3.19 Common Functions in Pandas
3.20 Statistical Functions in Pandas
3.21 Date and Timedelta
3.22 IO Tools
3.23 Categorical Data
3.24 Working with Text Data
3.25 Iteration
3.26 Plotting with Pandas
3.27 Matplotlib
3.28 Demo Matplotlib
3.29 Data Visualization Libraries in Python Matplotlib
3.30 Graph Types
3.31 Using Matplotlib to Plot Graphs
3.32 Matplotlib for 3d Visualization
3.33 Using Matplotlib with Other Python Packages
3.34 Data Visualization Libraries in Python Seaborn An introduction
3.35 Seaborn Visualization Features
3.36 Using Seaborn to Plot Graphs
3.37 Analysis using seaborn plots
3.38 Plotting 3D Graphs for Multiple Columns using Seaborn
3.39 Scipy
3.40 Demo Scipy
3.41 Scikit learn
3.42 Scikit Models
3.43 Scikit Datasets
3.44 Preprocessing Data in Scikit Learn Part 1
3.45 Preprocessing Data in Scikit Learn Part 2
3.46 Preprocessing Data in Scikit Learn Part 3
3.47 Demo Scikit learn
3.48 Key Takeaways

Statistics
4.1 Learning Objectives
4.2 Introduction to Linear Algebra
4.3 Scalars and vectors
4.4 Dot product of Two Vectors
4.5 Linear independence of Vectors
4.6 Norm of a Vector
4.7 Matrix
4.8 Matrix Operations
4.9 Transpose of a Matrix
4.10 Rank of a Matrix
4.11 Determinant of a matrix and Identity matrix or operator
4.12 Inverse of a matrix and Eigenvalues and Eigenvectors
4.13 Calculus in Linear Algebra
4.14 Importance of Statistics for Data Science
4.15 Common Statiscal Terms
4.16 Types of Statistics
4.17 Data Categorization and types of date
4.18 Levels of Measurement
4.19 Measures of central tendency mean
4.20 Measures of Central Tendency Median
4.21 Measures of Central Tendency Mode
4.22 Measures of Dispersion
4.23 Variance
4.24 Random Variables
4.25 Sets
4.26 Measure of Shape Skewness
4.27 Measure of Shape Kurtosis
4.28 Covariance and corelation
4.29 Basic Statistics with Python Problem Statement
4.30 Basic Statistics with Python Solution
4.31 Probability its Importance and Probability Distribution
4.32 Probability Distribution Binomial Distribution
4.33 Binomial Distribution using Python
4.34 Probability Distribution Poisson Distribution
4.35 Poisson Distribution Using Python
4.36 Probability Distribution Normal Distribution
4.37 Probability Distribution Uniform Distribution
4.38 Probability Distribution Bernoulli Distribution
4.39 Probability Density Function and Mass Function
4.40 Cumulative Distribution Function
4.41 Central Limit Theorem
4.42 Bayes Theorem
4.43 Estimatioin Theory
4.44 Point Estimate using Python
4.45 Distribution
4.46 Kurtosis Skewness and Student's T distribution
4.47 Hypothesis Testing and mechanism
4.48 Hypothesis Testing Outcomes Type I and II Errors
4.49 Null Hypothesis and Alternate Hypothesis
4.50 Confidence Intervals
4.51 Margin of Errors
4.52 Confidence Levels
4.53 T test and P values Using Python
4.54 Z test and P values Using Python
4.55 Comparing and Contranstin T test and Z tests
4.56 Chi Squared Distribution
4.57 Chi Squared Distribution using Python
4.58 Chi Squared Test and Goodness of Fit
4.59 ANOVA
4.60 ANOVA Terminologies
4.61 Assumptions and Types of ANOVA
4.62 Partition of Variance
4.63 F distribution
4.64 Distribution using Python
4.65 F Test
4.66 Advanced Statistics with Python Problem Statement
4.67 Advanced Statistics with Python Solution
4.68 Key Takeaways

Data Wrangling
5.1 Learning Objectives
5.2 Data Exploration Loading Files Part A
5.3 Data Exploration Loading Files Part B
5.4 Data Exploration Techniques Part A
5.5 Data Exploration Techniques Part B
5.6 Seaborn
5.7 Demo Correlation Analysis
5.8 Data Wrangling
5.9 Missing Values in a Dataset
5.10 Outlier Values in a Dataset
5.11 Demo Outlier and Missing Value Treatment
5.12 Data Manipulation
5.13 Functionalities of Data Object in Python Part A
5.14 Functionalities of Data Object in Python Part B
5.15 Different Types of Joins
5.16 Key Takeaways

Feature Engineering
6.1 Learning Objectives
6.2 Introduction to Feature Engineering
6.3 Encoding of Catogorical Variables
6.4 Label Encoding
6.5 Techniques used for Encoding variables
6.6 Key Takeaways

Exploratory Data Analysis
7.1 Learning Objectives
7.2 Types of Plots
7.3 Plots and Subplots
7.4  Assignment 01 Pairplot Demo
7.5  Assignment 02 Pairplot Demo
7.6 Key Takeaways

Feature Selection
8.1 Learning Objectives
8.2 Feature Selection
8.3 Regression
8.4 Factor Analysis
8.5 Factor Analysis Process
8.6 Key Takeaways