Data Analytics & Visualization: Acquire Demanded Tech Skills

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.