Includes
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Duration:
15 days
(Avg)
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Chapters: 142
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Total Hours: 14:43
Features
Full lifetime access
Access on mobile
Assignments
Certificate of Completion
Overview
Course Description
Embarking on a transformative journey into the dynamic realm of Data Analytics and Visualization promises to equip participants with essential and sought-after tech skills. This comprehensive course is meticulously designed to empower learners with proficiency in key tools and methodologies crucial for success in the field.
The primary learning objectives of this course include:
1. Python Proficiency: Participants will gain hands-on experience in Python, a versatile programming language widely used for data analysis and manipulation. Through practical exercises, learners will understand how to leverage Python libraries such as Pandas and NumPy for efficient data handling and manipulation.
2. Excel Mastery: Advanced skills in Excel will be developed, exploring its robust features for data organization, analysis, and visualization. Participants will harness the power of Excel functions and formulas to extract insights from complex datasets.
3. Statistical Foundations: A solid foundation in statistical concepts and techniques will be acquired, essential for making informed decisions based on data. Participants will learn to apply statistical methods to interpret and draw meaningful conclusions from datasets.
4. Data Analysis Process: The entire data analysis process will be explored, from data cleaning and preprocessing to exploratory data analysis (EDA) and feature engineering. Participants will learn how to identify patterns, outliers, and trends within datasets, enabling them to extract valuable insights.
5. Data Visualization: The art of presenting data visually will be mastered through a variety of visualization tools and techniques. Participants will use industry-standard tools like Matplotlib and Seaborn to create compelling and informative data visualizations.
Upon completion of the course, participants will possess a well-rounded skill set in data analytics and visualization. They will be equipped to tackle real-world challenges and contribute meaningfully to data-driven decision-making in any professional setting.
Joining this transformative journey promises to elevate participants into proficient and sought-after tech professionals in the field of data analytics and visualization. With these skills, participants will be well-positioned to excel in the ever-evolving landscape of data science and analytics.
What you'll learn
- Real-world use cases of Python and its versatility.
- Installation of Python on both Mac and Windows operating systems.
- Fundamentals of programming with Python, including variables and data types.
- Working with various operators in Python to perform operations.
- Fundamental concepts and importance of statistics in various fields.
- How to use statistics for effective data analysis and decision-making.
- Introduction to Python for statistical analysis, including data manipulation and visualization.
Requirements
- Students should have a general understanding of how to operate a computer.
- Be comfortable with common tasks like file management and using a web browser.
- No Prior Programming Experience Required.
- A basic understanding of mathematics, including algebra and arithmetic.
- Familiarity with fundamental concepts in data analysis and problem-solving.
Course Content
120 Lessons | 3 Quiz | 14:43 Total hours
Excel Fundamentals
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Excel Applications -
Understanding the Excel Interface 00:16:41
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Sorting and Filtering 00:15:17
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Conditional Formatting 00:08:59
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Quiz on Excel Fundamentals
Statistical and Mathematical Functions in Excel
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Introductions to Statistical Functions 00:09:39
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Introduction to Mathematical Functions 00:08:57
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Quiz on Statistical and Mathematical Functions
Lookup functions, and Pivot Tables
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Introduction to Lookup Functions
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Introduction to Index and Match 00:07:48
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Introduction to Pivot Tables 00:10:15
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Introduction to Pivot Charts
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Quiz on Lookup Functions, and Pivot Tables
Logical Functions, and Text Functions
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Introduction to Logical Function 00:06:35
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Formatting Cells based on Logical Functions 00:04:52
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Introduction to Text Functions 00:06:41
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Formatting cells based on Text Functions 00:09:21
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Quiz on Logical Functions, and Text Functions
Data Cleaning, and Feature engineering
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Introduction to Date and Time Functions 00:07:11
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Basics of Data Cleaning in Excel 00:09:16
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Basics of Feature Engineering in Excel 00:07:13
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Introduction to Power Query in Excel 00:05:10
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Quiz on Data Cleaning and Feature Engineering
What If analysis
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Scenario Manager 00:07:47
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Goal Seek 00:04:56
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Data Tables 00:17:35
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Solver Package
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Quiz on What If analysis
Charts and Dashboards
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Data Visualization Best Practices 00:09:11
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Types of Charts in Excel 00:04:54
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Creating and Formatting Charts 00:03:47
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Quiz on Charts and Dashboards
Linear Regression and Forecasting
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Introduction to Linear Regression... 00:10:10
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Preliminary Forecasting Analysis.... 00:06:50
Basics of Python
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Real world use cases of Python
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Installation of Anaconda for Windows and macOS 00:04:48
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Introduction to Variables 00:05:59
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Introduction to Data Types and Type Casting 00:06:27
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Scope of Variables 00:08:18
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Introduction to Operators 00:19:03
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Quiz on Basics of Python
Introduction to Data Structures
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Introduction to Lists and Tuples
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Introduction to Sets and Dictionaries 00:12:12
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Introduction to Stacks and Queues 00:06:57
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Introduction to Space and Time Complexity 00:13:27
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Introduction to Sorting Algorithms 00:08:36
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Introduction to Searching Algorithms 00:08:47
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Quiz on Data Structures
Introduction to Functions in Python
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Introduction to Parameters and Arguments
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Introduction to Python Modules 00:05:39
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Introduction to Filter, Map, and Zip Functions 00:13:44
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Introduction to List, Set and Dictionary Comprehensions 00:10:23
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Introduction to Lambda Functions 00:07:03
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Introduction to Analytical and Aggregate Functions 00:08:09
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Quiz on Functions in Python
Strings and Regular Expressions
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Introduction to Strings 00:05:44
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Introduction to Important String Functions 00:10:46
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Introduction to String Formatting and User Input 00:08:30
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Introduction to Meta Characters 00:22:30
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Introduction to Built-in Functions for Regular Expressions 00:07:44
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Special Characters and Sets for Regular Expressions 00:07:30
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Quiz on Strings and Regular Expressions
Loops and Conditionals
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Introduction to Conditional Statements 00:06:35
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Introduction to For Loops 00:04:18
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Introduction to While Loops
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Introduction to Break and Continue 00:03:09
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Using Conditional Statements in Loops 00:05:51
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Nested Loops and Conditional Statements 00:07:10
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Quiz on Loops and Conditionals
OOPs and Date-Time
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Introduction to OOPs Concept 00:05:21
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Introduction to Inheritance
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Introduction to Encapsulation 00:03:28
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Introduction to Polymorphism 00:05:23
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Introduction to Date and Time Class 00:07:53
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Introduction to TimeDelta Class 00:05:17
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Quiz on OOPs and Date-Time
Introduction to Statistics
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Explain the role of statistics in data analysis 00:05:22
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Introduction to Python for Statistical Analysis 00:05:31
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Introduction to Statistics and its importance 00:05:58
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Quiz on Introduction to Statistics
Introduction to Descriptive Statistics
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Types of Data
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Measures of Central Tendency 00:06:18
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Measures of Spread 00:04:37
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Measures of Dependence 00:05:06
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Measures of Shape and Position 00:09:37
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Measures of Standard Scores
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Quiz on Descriptive Statistics
Introduction to Basic and Conditional Probability
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Introduction to Basic Probability
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Introduction to Set Theory 00:06:58
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Introduction to Conditional Probability 00:05:12
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Introduction to Bayes Theorem 00:08:23
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Introduction to Permutations and Combinations 00:07:47
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Introduction to Random Variables 00:05:25
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Introduction to Probability Distribution Functions 00:13:43
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Quiz on Basic and Conditional Probability
Introduction to Inferential Statistics
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Introduction to Normal Distribution
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Introduction to Skewness and Kurtosis 00:09:01
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Introduction to Statistical Transformations 00:12:46
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Introduction to Sample and Population Mean 00:04:57
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Introduction to Central Limit Theorem 00:05:08
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Introduction to Bias and Variance 00:07:28
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Introduction to Maximum Likelihood Estimation 00:06:53
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Introduction to Confidence Intervals 00:05:12
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Introduction to Correlations 00:18:27
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Introduction to Sampling Methods 00:17:00
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Quiz on Inferential Statistics
Introduction to Hypothesis Testing
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Fundamentals of Hypothesis Testing 00:07:09
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Introduction to T Tests 00:09:00
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Introduction to Z Tests 00:05:37
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Introduction to Chi Squared Tests 00:15:16
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Introduction to Anova Tests 00:03:30
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Quiz on Hypothesis Testing
Introduction to Numpy and Pandas
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Introduction to Numpy Arrays 00:08:10
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Introduction to Numpy Operations 00:12:38
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Introduction to Pandas 00:05:54
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Introduction to Series and DataFrames 00:11:02
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Reading CSV and JSON Data using Pandas 00:03:56
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Analyzing the Data using Pandas 00:03:21
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Quiz on Introduction to Numpy and Pandas
Advanced Functions in Pandas
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Indexing, Selecting, and Filtering Data 00:14:54
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Merging and Concatenation using Pandas 00:07:29
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Correlation and Plotting using Pandas 00:09:30
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Introduction to Lambda, Map and Apply Functions 00:04:47
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Introduction to Grouping Operations using Pandas 00:08:59
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Introduction to Cross Tabulation using Pandas 00:03:46
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Introduction to Filtering Operations using Pandas 00:08:58
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Interactive Grouping and Filtering Operations 00:10:00
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Quiz on Advanced Functions in Pandas
Types of Charts and Visualizations
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Factors for good Data Visualization
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Introduction to Univariate Data Visualizations 00:09:10
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Introduction to Bivariate Data Visualizations 00:03:10
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Plotting two Categorical Variables 00:04:30
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Introduction to Multivariate Data Visualizations 00:04:29
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Introduction to Heatmaps and Pairplots 00:04:29
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Quiz on Types of Charts and Visualizations
Advanced Data Visualizations
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Introduction to Animated Data Visualizations using Plotly 00:10:24
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Colorscales, Facet Grids, and Sub plots 00:22:30
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Introduction to 3D Data Visualization 00:06:56
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Introduction to Interactive Data Visualization 00:09:00
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Introduction to Maps using Plotly 00:07:49
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Introduction to Funnel and Gantt Charts using Plotly 00:14:07
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Quiz on Advanced Data Visualizations
