Applied Data Science with Python Training Courses

Lesson 1：Course Introduction
1.1 Course Introduction
1.2 What you will Learn
Lesson 2：Introduction to Data Science
2.1 Introduction
2.2 Data Science and its Applications
2.3 The Data Science Process Part 1
2.4 The Data Science Process Part 2
2.5 Recap
Lesson 3：Python Libraries for Data Science
3.1 Introduction
3.2 Setting Up Jupyter Notebook Part 1
3.3 Setting Up Jupyter Notebook Part 2
3.4 Python Functions
3.5 Python Types and Sequences
3.6 Python Strings Deep Dive
3.7 Python Demo Reading and Writing csv files
3.8 Date and Time in Python
3.9 Objects in Python Map
3.10 Lambda and List Comprehension
3.11 Why Python for Data Analysis?
3.12 Python Packages for Data Science
3.13 StatsModels Package Part 1
3.14 StatsModels Package Part 2
3.15 Spicy Package
3.16 Recap
3.17 Spotlight
Lesson 4：NumPy
4.1 Introduction
4.2 Fundamentals of NumPy
4.3 Array shapes and axes in NumPy Part A
4.4 Array shapes and axes in NumPy Part B
4.5 Arithmetic Operations
4.6 Conditional Logic
4.7 Common Mathematical and Statistical Functions in Numpy
4.8 Indexing And Slicing Part 1
4.9 Indexing And Slicing Part 2
4.10 File Handling
4.11 Recap
Lesson 5：Data Wrangling
5.1 Introduction
5.2 Introduction to Linear Algebra
5.3 Scalars and Vectors
5.4 Dot Product of Two Vectors
5.5 Linear independence of Vectors
5.6 Norm of a Vector
5.7 Matrix
5.8 Matrix Operations
5.9 Transpose of a Matrix
5.10 Rank of a Matrix
5.11 Determinant of a matrix and Identity matrix or operator
5.12 Inverse of a matrix and Eigenvalues and Eigenvectors
5.13 Calculus in Linear Algebra
5.14 Recap
Lesson 6：Statistics Fundamentals
6.1 Introduction
6.2 Importance of Statistics with Respect to Data Science
6.3 Common Statistical Terms
6.4 Types of Statistics
6.5 Data Categorization and Types
6.6 Levels of Measurement
6.7 Measures of Central Tendency
6.8 Measures of Central Tendency
6.9 Measures of Central Tendency
6.10 Measures of Dispersion
6.11 Random Variables
6.12 Sets
6.13 Measures of Shape（Skewness）
6.14 Measurement of Shape（Kurtosis）
6.15 Covariance and Correlation
6.16 Recap
Lesson 7：Probability Distribution
7.1 Introduction
7.2 Probability its Importance and Probability Distribution
7.3 Probability Distribution Binomial Distribution
7.4 Probability Distribution Poisson Distribution
7.5 Probability Distribution Normal Distribution
7.6 Probability Distribution Uniform Distribution
7.7 Probability Distribution Bernoulli Distribution
7.8 Probability Density Function and Mass Function
7.9 Cumulative Distribution Function
7.10 Central Limit Theorem
7.11 Estimation Theory
7.12 Recap
Lesson 8: Advanced Statistics
8.1 introduction
8.2 Distribution
8.3 Kurtosis Skewness and Student's T-distribution
8.4 Hypothesis Testing and Mechanism
8.5 Hypothesis Testing Outcomes: Type land ll Errors
8.6 Null Hypothesis and AlternateHypothesis
8.7 Confidence Intervals
8.8 Margins of error
8.9 Confidence Leve!
8.10 T-Test and p- values (Lab)
8.11 Z - Test and p- values
 8.12 Comparing and Contrasting T test andZ test
8.13 Bayes Theorem
8.14 Chi Sqare Distribution
8.15 Chi Square Distribution :Demo
8.16 Chi Square Test and Goodness of Fit
8.17 Analysis of Variance or ANOVA
8.18 ANOVA Termonologies
8.19 Assumptions and Types Of ANOVA
8.20 Partition of Variance using Python
8.21F-Distribution
8.22 F- Distribution using Python
8.23 F- Test
8.24 Recap
8.25 Spotlight
Lesson 9: Pandas
9.1 introduction
9.2 Introduction to Pandas
9.3 Pandas Series
9.4 Querying a Series
9.5 Pandas Dataframes
9.6 Pandas Panel
9.7 Common Functions In Pandas
9.8 Pandas Functions Data StatisticalFunction, Windows Function
9.9 Pandas Function Data and Timedelta
9.10 l0 Tools Explain all the read function
9.11 Categorical Data
9.12 Working with Text Data
9.13 lteration
9.14 Sorting
9.15 Plotting with Pandas
9.16 Recap
Lesson 10: Data Analysis
10.1 Introduction
10.2 Understanding Data
10.3 Types of Data StructuredUnstructured Messy etc
10.4 Working with Data Choosingappropriate tools, Data collection, Data w..
10.5 mporting and Exporting Data inPython
10.6 Reqular Expressions in Python
10.7 Manipulating text with RegularExpressions
10.8 Accessing databases in Python
10.9 Recap
10.10 Spotlight
Lesson 11: Data Wrangling
11.1 introduction
11.2 Pandorable or ldiomatic Pandas Code
11.3 Loading Indexing and Reindexing
11.4 Merging
11.5 Memory Optimization in Python
11.6 Data Pre Processing:Data Loading and Dropping Null Values
11.7 Data Pre-processing Filling NullValues
11.8 Data Binning Formatting and Normalization
11.9 Data Binning Standardization
11.10 Describing Data
11.11 Recap
Lesson 12: Data Visualization
12.1 Introduction
12.2 Principles of information visualization
12.3 Visualizing Data using Pivot Tables
12.04 Data Visualization Libraries in Python Matplotlib
12.5 Graph Types
12.6 Data Visualization Libraries in Python Seaborn
12.7 Data Visualization Libraries in Python Seaborn
12.8 Data Visualization Libraries in Python Plotly
12.9 Data Visualization Libraries in Python Plotly
12.10 Data Visualization Libraries in Python Bokeh
12.11 Data Visualization Libraries in Python Bokeh
12.12 Using Matplotlib to Plot Graphs
12.13 Plotting 3D Graphs for Multiple Columns using Matplotlib
12.14 Using Matplotlib with other python packages
12.15 Using Seaborn to Plot Graphs
12.16 Using Seaborn to Plot Graphs
12.17 Plotting 3D Graphs for MultipleColumns Using Seaborn
12.18 Introduction to Plotly
12.18 introduction to Plotly
 12.19 Introduction to Bokeh
 12.20 Recap
Lesson 13: End to End Statistics Application with Python
13.1 introduction
13.2 Basic Statistics with Python Problem Statement
13.3 Basic Statistics with Python Solution
13.4 Scipy for Statistics ProblemStatement
13.5 Scipy For Statistics Solution
13.6 Advanced Statistics Python
13.7 Advanced Statistics with PythonSolution
13.8 Recap
13.9 Spotlight