Data Analytics & Visualization: Acquire Demanded Tech Skills
Published 11/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 14.73 GB | Duration: 17h 9m
Published 11/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 14.73 GB | Duration: 17h 9m
Unlocking Insights through Data: Mastering Analytics and Visualization for In-Demand Tech Proficiency
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.
Description
Embark on a transformative journey into the dynamic realm of Data Analytics and Visualization, where you will acquire essential and sought-after tech skills. This comprehensive course is designed to empower you with proficiency in key tools and methodologies, including Python programming, Excel, statistical analysis, data analysis, and data visualization.Key Learning Objectives:- Gain hands-on experience in Python, a powerful and versatile programming language widely used for data analysis and manipulation.- Learn to leverage Python libraries such as Pandas and NumPy for efficient data handling and manipulation.- Develop advanced skills in Excel, exploring its robust features for data organization, analysis, and visualization.- Harness the power of Excel functions and formulas to extract insights from complex datasets.- Acquire a solid foundation in statistical concepts and techniques essential for making informed decisions based on data.- Apply statistical methods to interpret and draw meaningful conclusions from data sets.- Explore the entire data analysis process, from data cleaning and preprocessing to exploratory data analysis (EDA) and feature engineering.- Learn how to identify patterns, outliers, and trends within datasets, enabling you to extract valuable insights.- Master the art of presenting data visually through a variety of visualization tools and techniques.- Use industry-standard tools like Matplotlib and Seaborn to create compelling and informative data visualizations.Upon completion, you will possess a well-rounded skill set in data analytics and visualization, equipping you to tackle real-world challenges and contribute meaningfully to data-driven decision-making in any professional setting. Join us on this journey to become a proficient and sought-after tech professional in the field of data analytics and visualization.
Overview
Section 1: Fundamentals of Excel
Lecture 1 Excel Applications
Lecture 2 Understanding the Excel Interface
Lecture 3 Sorting and Filtering
Lecture 4 Conditional Formatting
Section 2: Statistical and Mathematical Functions in Excel
Lecture 5 Introductions to Statistical Functions
Lecture 6 Introduction to Mathematical Functions
Lecture 7 Introduction to Financial Functions…….
Section 3: Lookup functions, and Pivot Tables
Lecture 8 Introduction to Lookup Functions
Lecture 9 Introduction to Index and Match
Lecture 10 Introduction to Pivot Tables
Lecture 11 Introduction to Pivot Charts
Section 4: Logical Functions, and Text Functions
Lecture 12 Introduction to Logical Function
Lecture 13 Formatting Cells based on Logical Functions
Lecture 14 Introduction to Text Functions
Lecture 15 Formatting cells based on Text Functions
Section 5: Data Cleaning, and Feature engineering
Lecture 16 Introduction to Date and Time Functions
Lecture 17 Basics of Data Cleaning in Excel
Lecture 18 Basics of Feature Engineering in Excel
Lecture 19 Introduction to Power Query in Excel
Section 6: What If analysis
Lecture 20 Scenario Manager
Lecture 21 Goal Seek
Lecture 22 Data Tables
Lecture 23 Solver Package
Section 7: Charts and Dashboards
Lecture 24 Data Visualization Best Practices
Lecture 25 Types of Charts in Excel
Lecture 26 Creating and Formatting Charts
Lecture 27 Creating and Formatting Dashboards……
Section 8: Linear Regression and Forecasting
Lecture 28 Introduction to Linear Regression…
Lecture 29 Preliminary Forecasting Analysis….
Lecture 30 Simple Forecasting Methods….
Lecture 31 Powerful Forecasting Methods…..
Section 9: Python
Lecture 32 Real world use cases of Python
Lecture 33 Installation of Anaconda for Windows and macOS
Lecture 34 Introduction to Variables
Lecture 35 Introduction to Data Types and Type Casting
Lecture 36 Scope of Variables
Lecture 37 Introduction to Operators
Lecture 38 Introduction to Lists and Tuples
Lecture 39 Introduction to Sets and Dictionaries
Lecture 40 Introduction to Stacks and Queues
Lecture 41 Introduction to Space and Time Complexity
Lecture 42 Introduction to Sorting Algorithms
Lecture 43 Introduction to Searching Algorithms
Lecture 44 Introduction to Parameters and Arguments
Lecture 45 Introduction to Python Modules
Lecture 46 Introduction to Filter, Map, and Zip Functions
Lecture 47 Introduction to Lambda Functions
Lecture 48 Introduction to List, Set and Dictionary Comprehensions
Lecture 49 Introduction to Analytical and Aggregate Functions
Lecture 50 Introduction to Strings
Lecture 51 Introduction to Important String Functions
Lecture 52 Introduction to String Formatting and User Input
Lecture 53 Introduction to Meta Characters
Lecture 54 Introduction to Built-in Functions for Regular Expressions
Lecture 55 Special Characters and Sets for Regular Expressions
Lecture 56 Introduction to Conditional Statements
Lecture 57 Introduction to For Loops
Lecture 58 Introduction to While Loops
Lecture 59 Introduction to Break and Continue
Lecture 60 Using Conditional Statements in Loops
Lecture 61 Nested Loops and Conditional Statements
Lecture 62 Introduction to OOPs Concept
Lecture 63 Introduction to Inheritance
Lecture 64 Introduction to Encapsulation
Lecture 65 Introduction to Polymorphism
Lecture 66 Introduction to Date and Time Class
Lecture 67 Introduction to TimeDelta Class
Section 10: Statistics and Hypothesis Testing for Data science
Lecture 68 Introduction to Statistics and its importance
Lecture 69 Explain the role of statistics in data analysis
Lecture 70 Introduction to Python for Statistical Analysis
Lecture 71 Types of Data
Lecture 72 Measures of Central Tendency
Lecture 73 Measures of Spread
Lecture 74 Measures of Dependence
Lecture 75 Measures of Shape and Position
Lecture 76 Measures of Standard Scores
Lecture 77 Introduction to Basic Probability
Lecture 78 Introduction to Set Theory
Lecture 79 Introduction to Conditional Probability
Lecture 80 Introduction to Bayes Theorem
Lecture 81 Introduction to Permutations and Combinations
Lecture 82 Introduction to Random Variables
Lecture 83 Introduction to Probability Distribution Functions
Lecture 84 Introduction to Normal Distribution
Lecture 85 Introduction to Skewness and Kurtosis
Lecture 86 Introduction to Statistical Transformations
Lecture 87 Introduction to Sample and Population Mean
Lecture 88 Introduction to Central Limit Theorem
Lecture 89 Introduction to Bias and Variance
Lecture 90 Introduction to Maximum Likelihood Estimation
Lecture 91 Introduction to Confidence Intervals
Lecture 92 Introduction to Correlations
Lecture 93 Introduction to Sampling Methods
Lecture 94 Fundamentals of Hypothesis Testing
Lecture 95 Introduction to T Tests
Lecture 96 Introduction to Z Tests
Lecture 97 Introduction to Chi Squared Tests
Lecture 98 Introduction to Anova Tests
Section 11: Data Analysis and Data Viz
Lecture 99 Introduction to Numpy Arrays
Lecture 100 Introduction to Numpy Operations
Lecture 101 Introduction to Pandas
Lecture 102 Introduction to Series and DataFrames
Lecture 103 Reading CSV and JSON Data using Pandas
Lecture 104 Analyzing the Data using Pandas
Section 12: Advanced Functions in Pandas
Lecture 105 Indexing, Selecting, and Filtering Data
Lecture 106 Merging and Concatenation using Pandas
Lecture 107 Correlation and Plotting using Pandas
Lecture 108 Introduction to Lambda, Map and Apply Functions
Lecture 109 Introduction to Grouping Operations using Pandas
Lecture 110 Introduction to Cross Tabulation using Pandas
Lecture 111 Introduction to Filtering Operations using Pandas
Lecture 112 Interactive Grouping and Filtering Operations
Section 13: Types of Charts and Visualizations
Lecture 113 Factors for good Data Visualization
Lecture 114 Introduction to Univariate Data Visualizations
Lecture 115 Introduction to Bivariate Data Visualizations
Lecture 116 Plotting two Categorical Variables
Lecture 117 Introduction to Multivariate Data Visualizations
Lecture 118 Introduction to Heatmaps and Pairplots
Section 14: Advanced Data Visualizations
Lecture 119 Colorscales, Facet Grids, and Sub plots
Lecture 120 Introduction to 3D Data Visualization
Lecture 121 Introduction to Interactive Data Visualization
Lecture 122 Introduction to Maps using Plotly
Lecture 123 Introduction to Funnel and Gantt Charts using Plotly
Lecture 124 Introduction to Animated Data Visualizations using Plotly
Beginners with no prior programming experience.,Students or professionals in various fields, including business, science, social sciences, and healthcare, who want to enhance their data analysis skills.,Anyone interested in automating tasks or data analysis.,Data analysts, researchers, and scientists seeking to strengthen their statistical foundations and Python programming skills.,Beginners with no prior statistical knowledge but with a curiosity to learn and apply statistical methods.,Professionals looking to advance their career by acquiring valuable statistical and data analysis skills.