Data Science Mastery 2025: Excel, Python & Tableau

Excel
1 Excel Applications
2 Understanding the Excel Interface
3 Sorting and Filtering
4 Conditional Formatting
5 Introductions to Statistical Functions
6 Introduction to Mathematical Functions
7 Introduction to Lookup Functions
8 Introduction to Index and Match
9 Introduction to Pivot Tables
10 Introduction to Pivot Charts
11 Introduction to Logical Function
12 Formatting Cells based on Logical Functions
13 Introduction to Text Functions
14 Formatting cells based on Text Functions
15 Introduction to Date and Time Functions
16 Basics of Data Cleaning in Excel
17 Basics of Feature Engineering in Excel
18 Introduction to Power Query in Excel
19 Scenario Manager
20 Goal Seek
21 Data Tables
22 Solver Package
23 Data Visualization Best Practices
24 Types of Charts in Excel
25 Creating and Formatting Charts
26 Introduction to Linear Regression
27 Preliminary Forecasting Analysis

Tableau
28 Introduction to Tableau
29 Why Tableau and its Importance
30 Different type of Tableau Versions
31 Installation of Tableau Public Version
32 Installation of Tableau Desktop Version
33 Introduction to Dimensions and Measures
34 Introduction to Measure Names and Values
35 Introduction to Discrete and Continuous field
36 Introduction to Show Me Toolbar
37 Text Chart
38 Highlight Tables
39 Bar chart
40 Line chart
41 Pie Chart
42 Bubble Chart
43 Histogram
44 Heat Map
45 Tree Map
46 Area Chart
47 Dual Axis Chart
48 Scatter Plot
49 Bullet Chart
50 Waterfall Chart
51 Gantt Chart

Python
52 Real world use cases of Python
53 Installation of Anaconda for Windows and macOS
54 Introduction to Variables
55 Introduction to Data Types and Type Casting
56 Scope of Variables
57 Introduction to Operators
58 Introduction to Lists and Tuples
59 Introduction to Sets and Dictionaries
60 Introduction to Stacks and Queues
61 Introduction to Space and Time Complexity
62 Introduction to Sorting Algorithms
63 Introduction to Searching Algorithms
64 Introduction to Parameters and Arguments
65 Introduction to Python Modules
66 Introduction to Filter, Map, and Zip Functions
67 Introduction to List, Set and Dictionary Comprehensions
68 Introduction to Lambda Functions
69 Introduction to Analytical and Aggregate Functions
70 Introduction to Strings
71 Introduction to Important String Functions
72 Introduction to String Formatting and User Input
73 Introduction to Meta Characters
74 Introduction to Built-in Functions for Regular Expressions
75 Special Characters and Sets for Regular Expressions
76 Introduction to Conditional Statements
77 Introduction to For Loops
78 Introduction to While Loops
79 Introduction to Break and Continue
80 Using Conditional Statements in Loops
81 Nested Loops and Conditional Statements
82 Introduction to OOPs Concept
83 Introduction to Inheritance
84 Introduction to Encapsulation
85 Introduction to Polymorphism
86 Introduction to Date and Time Class
87 Introduction to TimeDelta Class

Statistics
88 Introduction to Statistics and its importance
89 Explain the role of statistics in data analysis
90 Introduction to Python for Statistical Analysis
91 Types of Data
92 Measures of Central Tendency
93 Measures of Spread
94 Measures of Dependence
95 Measures of Shape and Position
96 Measures of Standard Scores
97 Introduction to Basic Probability
98 Introduction to Set Theory
99 Introduction to Conditional Probability
100 Introduction to Bayes Theorem
101 Introduction to Permutations and Combinations
102 Introduction to Random Variables
103 Introduction to Probability Distribution Functions
104 Introduction to Normal Distribution
105 Introduction to Skewness and Kurtosis
106 Introduction to Statistical Transformations
107 Introduction to Sample and Population Mean
108 Introduction to Central Limit Theorem
109 Introduction to Bias and Variance
110 Introduction to Maximum Likelihood Estimation
111 Introduction to Confidence Intervals
112 Introduction to Correlations
113 Introduction to Sampling Methods
114 Fundamentals of Hypothesis Testing
115 Introduction to T Tests
116 Introduction to Z Tests
117 Introduction to Chi Squared Tests
118 Introduction to Anova Tests

Data Analysis and Data Visualization
119 Introduction to Numpy and Pandas
120 Introduction to Numpy Operations
121 Introduction to Pandas
122 Introduction to Series and DataFrames
123 Reading CSV and JSON Data using Pandas
124 Analyzing the Data using Pandas
125 Indexing, Selecting, and Filtering Data
126 Merging and Concatenation using Pandas
127 Correlation and Plotting using Pandas
128 Introduction to Lambda, Map and Apply Functions
129 Introduction to Grouping Operations using Pandas
130 Introduction to Cross Tabulation using Pandas
131 Introduction to Filtering Operations using Pandas
132 Interactive Grouping and Filtering Operations
133 Factors for good Data Visualization
134 Introduction to Univariate Data Visualizations
135 Introduction to Bivariate Data Visualizations
136 Plotting two Categorical Variables
137 Introduction to Multivariate Data Visualizations
138 Introduction to Heatmaps and Pairplots
139 Colorscales, Facet Grids, and Sub plots
140 Introduction to 3D Data Visualization
141 Introduction to Interactive Data Visualization
142 Introduction to Maps using Plotly
143 Introduction to Funnel and Gantt Charts using Plotly
144 Introduction to Animated Data Visualizations using Plotly

Data Cleaning
145 Causes and Impact of Missing Values
146 Types of Missing Values
147 When to delete the Missing Values from Data
148 Imputing Missing Values with Statistical Values
149 Imputing Missing Values with Business Logic
150 Impact of Outliers on ML Models
151 Dealing with Outliers in an dataset

Introduction to Categorical Encoding Techniques
152 Introduction to Label and Ordinal Encoding
153 Introduction to Binary and BaseN Encoding
154 Introduction to Target Introduction Encoding

Introduction to Data Manipulation Functions
155 Introduction to reindex, set_index, reset_index, and sort_index Functions
156 Introduction to Replace and Droplevel Functions
157 Introduction to Stack and Unstack Functions

Introduction to Feature Engineering Techniques
158 Determining how to drop unnecessary columns
159 Decomposing the Date and Time Features
160 Decomposing the Categorical Features
161 Binning the Numerical Features
162 Aggregation of Features