Data Analytics 360: Become Data Analyst In Python & Excel

Master Python and Excel - 2 Widely Used Tools for A-Z Data Analysis with Complete Foundations and Hands-on Applications.

What you'll learn

You will master the fundamentals of data analytics, including facts and theories, statistical analysis, hypothesis testing, and machine learning.

You will learn how to apply conditional formatting in Excel to visually highlight key trends, insights, and anomalies within your data.

You will learn essential Excel formulas and functions such as SUM, AVERAGE, COUNT, IF statements and MORE, enabling you to manipulate data effectively.

You will learn to utilize Excel's lookup functions (VLOOKUP, HLOOKUP, XLOOKUP) to efficiently search for and retrieve specific information within datasets.

You will learn various graph and chart types in Excel for data visualization, including bar charts, pie charts, scatter plots, and more to communicate insights.

You will learn advanced analysis using PivotTables and PivotCharts, enabling you to analyze, and visualize complex datasets with ease and interactivity.

You will learn to use Excel's built-in data analysis tools for statistical analysis, i.e., descriptive statistics, t-tests, ANOVA, correlation, and regression.

You will learn to design and create dynamic DASHBOARD in Excel, by a visually interactive format for effective decision-making and reporting.

You will learn the important Python programming basics such as variables naming, data types, lists, dictionaries, dataframes, sets, loops, functions etc.

You will master a range of methods and techniques for data cleaning, sorting, filtering, data manipulation, transformation, and data preprocessing in Python.

You will learn to use Python for data visualizations, exploratory data analysis, statistical analysis, hypothesis testing methods and machine learning models.

You will work on practical data analysis projects to apply learned skills. Enhance problem-solving abilities through hands-on data analysis exercises.

Requirements

Access to computer and internet

Basic computer literacy

No coding experience required

Dedication, patience and perseverance

Description

Are you ready to embark on a journey into the world of data analytics? Welcome to Data Analytics 360, where you'll master two of the most powerful tools in the field: Python and Excel. In this comprehensive course, you'll dive deep into the foundations of data analysis, from basic statistical concepts to advanced machine learning techniques.Master the Fundamentals: Gain a solid understanding of data analytics principles, including statistical analysis, hypothesis testing, and machine learning. Whether you're new to the field or looking to sharpen your skills, this course provides the perfect starting point.Excel for Data Analysis: Unlock the full potential of Excel as a data analysis tool. Learn essential formulas and functions, harness the power of conditional formatting to identify trends and anomalies, and utilize lookup functions for efficient data retrieval. Discover the art of data visualization with various chart types and master advanced analysis with PivotTables and PivotCharts.Python Essentials: Dive into Python programming basics, from variables and data types to loops and functions. Explore methods for data cleaning, sorting, filtering, and manipulation, as well as techniques for exploratory data analysis and hypothesis testing. Harness the power of Python libraries for data visualization and machine learning.Hands-on Projects: Put your skills to the test with practical data analysis projects. From cleaning and preprocessing data to building machine learning models, you'll tackle real-world challenges and enhance your problem-solving abilities along the way.Become a Data Analyst: By the end of this course, you'll have the knowledge and skills to excel as a data analyst. Whether you're looking to advance your career or explore new opportunities, Data Analytics 360 equips you with the tools you need to succeed in the world of data.Enroll now and take the first step towards becoming a proficient data analyst with Data Analytics 360.

Overview

Section 1: All You Need to Know about Data Analysis

Lecture 1 Data analysis definition, types and examples

Lecture 2 Key components of data analysis

Lecture 3 Tools and technologies for data analysis

Lecture 4 Real-world application of data analysis

Section 2: Data Collection: Methods and Considerations

Lecture 5 Various sources of collecting data

Lecture 6 Population v/s sample and its methods

Section 3: Understand Data Cleaning and Its Methods

Lecture 7 Why you cannot ignore cleaning your data

Lecture 8 Various aspects of data cleaning

Section 4: Explore Joining and Concatenating Methods

Lecture 9 Various aspects of Joining datasets

Lecture 10 Adding extra data with concatenation

Section 5: Complete Picture of Exploratory Data Analysis

Lecture 11 EDA for generating significant insights

Lecture 12 Methods of exploratory data analysis Part 1

Lecture 13 Methods of exploratory data analysis Part 2

Lecture 14 Methods of exploratory data analysis Part 3

Section 6: Everything about Statistical Data Analysis

Lecture 15 The application of statistical test

Lecture 16 Types of statistical data analysis

Lecture 17 Statistical test v/s Exploratory data analysis

Lecture 18 A Recap on descriptive statistics methods

Lecture 19 Inferential statistics Part 1 – T-tests and ANOVA

Lecture 20 Inferential statistics Part 2 – Relationships measures

Lecture 21 Inferential statistics Part 3 – Linear regression

Section 7: Concepts of Probabilities in Data Analysis

Lecture 22 Probability in data analysis

Lecture 23 Classical probability

Lecture 24 Empirical probability

Lecture 25 Conditional probability

Lecture 26 Joint probability

Section 8: Hypothesis Testing in Statistical Analysis

Lecture 27 Hypothesis testing for inferential statistics

Lecture 28 Selecting statistical test and assumption testing

Lecture 29 Confidence level, significance level, p-value

Lecture 30 Making decision and conclusion on findings

Lecture 31 Complete statistical analysis and hypothesis testing

Section 9: Explore Data Transformation and Its Methods

Lecture 32 Transforming data for improved analysis

Lecture 33 Techniques for data transformation Part 1

Lecture 34 Techniques for data transformation Part 2

Section 10: Machine Learning for Predictive Efficiency

Lecture 35 ML for data analysis and decision-making

Lecture 36 Widely used ML methods in the data analytics

Lecture 37 Steps in developing machine learning model

Section 11: Explore Data Visualizations and Its Methods

Lecture 38 Visualizing data for the best insight delivery

Lecture 39 Several methods of data visualization Part 1

Lecture 40 Several methods of data visualization Part 2

Lecture 41 Several methods of data visualization Part 3

Section 12: Excel - Data Cleaning and Formatting

Lecture 42 Identifying and removing duplicates

Lecture 43 Dealing with missing values

Lecture 44 Dealing with outliers

Lecture 45 Finding and imputing inconsistent values

Lecture 46 Text-to-columns for data separation

Section 13: Excel - Data Sorting and Filtering

Lecture 47 Applying sorts & filters to narrow down data

Lecture 48 Advanced filtering with custom criteria

Section 14: Excel - Apply Conditional Formatting

Lecture 49 Highlighting cells based on criteria

Lecture 50 Findings top and bottom insights

Lecture 51 Creating color scales and color bars

Section 15: Excel - Formulas and Functions for Data Analysis

Lecture 52 SUM, AVERAGE, MIN, and MAX functions

Lecture 53 SUMIF, and AVERAGEIF functions

Lecture 54 COUNT, COUNTA, and COUNTIF functions

Lecture 55 YEAR, MONTH and DAY for date manipulation

Lecture 56 IF STATEMENTs for conditional operation

Lecture 57 VLOOKUP for column-wise insight search

Lecture 58 HLOOKUP for row-wise insight search

Lecture 59 XLOOKUP for robust & complex insight search

Section 16: Excel - Graphs and Charts for Data Visualization

Lecture 60 Analyze data with Stacked and cluster bar charts

Lecture 61 Analyze data with Pie chart and line chart

Lecture 62 Analyze data with Area chart and TreeMap

Lecture 63 Analyze data with Boxplot and Histogram

Lecture 64 Analyze data with Scatter plot and Combo chart

Lecture 65 Adjusting and decorating graphs and charts

Section 17: Excel - Data Analysis in PivotTables and PivotCharts

Lecture 66 PivotTables for GROUP data analysis PART 1

Lecture 67 PivotTables for CROSSTAB data analysis PART 2

Lecture 68 PivotCharts and Slicers for interactivity

Section 18: Excel - Data Analysis ToolPack for Statistical Analysis

Lecture 69 Descriptive statistics and analysis

Lecture 70 Independent sample t-test for two samples

Lecture 71 Paired sample t-test for two samples

Lecture 72 Analysis of variance – One way ANOVA

Lecture 73 Correlation analysis for relationship

Lecture 74 Multiple linear regression analysis

Section 19: Excel - Creating Interactive Dashboard

Lecture 75 Accumulating relevant information

Lecture 76 Creating a canvas for dashboard

Lecture 77 Developing the complete dashboard

Lecture 78 Final touch up for dashboard decoration

Section 20: Project 1 - Bank Churn Data Analysis

Section 21: Setting Up Python and Jupyter Notebook

Lecture 79 Installing Python and Jupyter Notebook – Mac

Lecture 80 Installing Python and Jupyter Notebook – Windows

Lecture 81 More alternative methods – Check the article

Lecture 82 Resources used for this section

Section 22: Python - Starting with Variables to Data Types

Lecture 83 Getting started with first python code

Lecture 84 Assigning variable names correctly

Lecture 85 Various data types and data structures

Lecture 86 Converting and casting data types

Lecture 87 Starting with Variables to Data Types

Section 23: Python - Operators in Python Programming

Lecture 88 Arithmetic operators (+, -, *, /, %, **)

Lecture 89 Comparison operators (>, <, >=, <=, ==, !=)

Lecture 90 Logical operators (and, or, not)

Lecture 91 Operators in Python Programming

Section 24: Python - Dealing with Data Structures

Lecture 92 Lists: creation, indexing, slicing, modifying

Lecture 93 Sets: unique elements, operations

Lecture 94 Dictionaries: key-value pairs, methods

Lecture 95 Several data structures

Section 25: Python - Conditionals Looping and Functions

Lecture 96 Conditional statements (if, elif, else)

Lecture 97 Nested logical expressions in conditions

Lecture 98 Looping structures (for loops, while loops)

Lecture 99 Defining, creating, and calling functions

Lecture 100 Conditionals Looping and Functions

Section 26: Python - Sequential Cleaning and Modifying Data

Lecture 101 Preparing notebook and loading data

Lecture 102 Identifying missing or null values

Lecture 103 Method of missing value imputation

Lecture 104 Exploring data types in a dataframe

Lecture 105 Dealing with inconsistent values

Lecture 106 Assigning correct data types

Lecture 107 Dealing with duplicated values

Lecture 108 Sequential data cleaning and modifying

Section 27: Python - Various Methods of Data Manipulation

Lecture 109 Sorting data by column and order

Lecture 110 Filtering data with boolean indexing

Lecture 111 Query method for precise filtering

Lecture 112 Filtering data with isin method

Lecture 113 Slicing dataframe with loc and iloc

Lecture 114 Filtering data for many conditions

Lecture 115 Various methods of data manipulation

Section 28: Python - Merging and Concatenating Dataframes

Lecture 116 Joining dataframes horizontally

Lecture 117 Concatenate dataframes vertically

Lecture 118 Merging and joining dataframes

Section 29: Python - Applied Exploratory Data Analysis Methods

Lecture 119 Frequency and percentage analysis

Lecture 120 Descriptive statistics and analysis

Lecture 121 Group by data analysis method

Lecture 122 Pivot table analysis - all in one

Lecture 123 Cross-tabulation analysis method

Lecture 124 Correlation analysis for numeric data

Lecture 125 Applied exploratory data analysis

Section 30: Python - Exploring Data Visualisations Methods

Lecture 126 Understanding visualisation tools

Lecture 127 Getting started with bar charts

Lecture 128 Stacked and clustered bar charts

Lecture 129 Pie chart for percentage analysis

Lecture 130 Line chart for grouping data analysis

Lecture 131 Exploring distribution with histogram

Lecture 132 Correlation analysis via scatterplot

Lecture 133 Matrix visualisation with heatmap

Lecture 134 Boxplot statistical visualisation method

Lecture 135 Exploring data visualisations methods

Section 31: Python - Practical Data Transformation Methods

Lecture 136 Investigating distribution of numeric data

Lecture 137 Shapiro Wilk test of normality

Lecture 138 Starting with square root transformation

Lecture 139 Logarithmic transformation method

Lecture 140 Box-cox power transformation method

Lecture 141 Yeo-Johnson power transformation method

Lecture 142 Practical data transformation methods

Section 32: Python - Statistical Tests and Hypothesis Testing

Lecture 143 One sample t-test

Lecture 144 Independent sample t-test

Lecture 145 One way Analysis of Variance

Lecture 146 Chi square test for independence

Lecture 147 Pearson correlation analysis

Lecture 148 Linear regression analysis

Lecture 149 Statistical tests and hypothesis testing

Section 33: Python - Exploring Feature Engineering Methods

Lecture 150 Generating new features

Lecture 151 Extracting day, month and year

Lecture 152 Encoding features - LabelEncoder

Lecture 153 Categorizing numeric feature

Lecture 154 Manual feature encoding

Lecture 155 Converting features into dummy

Lecture 156 Feature engineering methods

Section 34: Python - Data Preprocessing for Machine Learning

Lecture 157 Selecting features and target

Lecture 158 Scaling features - StandardScaler

Lecture 159 Scaling features - MinMaxScaler

Lecture 160 Dimensionality reduction with PCA

Lecture 161 Splitting into train and test set

Lecture 162 Preprocessing for machine learning

Section 35: Python - Supervised Regression ML Models

Lecture 163 Linear regression ML model

Lecture 164 Decision tree regressor ML model

Lecture 165 Random forest regressor ML model

Lecture 166 Supervised regression ML models

Section 36: Python - Supervised Classification ML Models

Lecture 167 Logistic regression ML model

Lecture 168 Decision tree classification ML model

Lecture 169 Random forest classification ML model

Lecture 170 Supervised classification ML models

Section 37: Python - Segmentation with KMeans Clustering

Lecture 171 Calculating within cluster sum of squares

Lecture 172 Selecting optimal number of clusters

Lecture 173 Application of KMeans machine learning

Lecture 174 Data segmentation with KMeans clustering

Section 38: Project 2 - Sports Data Analytics

Section 39: Resources - Python and Excel

Lecture 175 Extra note on functions and shortcuts

Lecture 176 Extra note on python data analysis

Those who are highly interested in learning complete data analytics using Python.,Individuals aiming to develop comprehensive knowledge in data cleaning, analysis, visualization, and dashboard creation in Excel.,This course is NOT for those who are interested to learn data science or advanced machine learning application.