Data Analysis by Excel, SQL, Python and Power BI

Name: Data Analysis by Excel, SQL, Python and Power BI
Rating: 3.6 (90 reviews)

Become a Data Analyst or Business Analyst using Data Mining, Data Wrangling, and Data Cleaning. Master course all in one

Created bySparky Academy

Last updated 10/2023

English

What you'll learn

You will able to analyze Raw Data patterns and uncover most of the hidden Information.
Your research strategies will improve by using basics and Advanced functions of Excel, SQL, and Python.
Confidently use the most crucial Excel functions and techniques for analysis
You will get Hand-on Experience using Excel, SQL, Python, and Power BI.
You will learn Data Wrangling, Data Cleaning, Data Analyzation and Data Manipulation.
You will Identify Ideas and manage Business Decisions.
Becomes an Expert in Data Storytelling and Optimize the overall output using Power BI.
Able to provide your thought on crucial situations and solve them accordingly.
You will be able to code on Python and SQL.
You will merge different Datasets using Python, Excel and Power-BI.

Course content

7 sections • 96 lectures • 13h 2m total length

Introduction to Data Analytics? Is it worth it ?2:52
Why Data Analytics?3:35
Types of Data Analysis7:31
Framework of Data Analytics Course5:54
The Course Content15:11
The Services you will provide.0:28
Future Trends1:48
Quiz

Overview of Excel Section8:40
Format, clean, and analyze data in Excel using charts and graphs for clear visualization. Master data connections, formatting rules, data quality checks, and descriptive statistics to derive insights.
Introduction to Excel6:46
Data Connections7:27
Discover data connections in Excel, importing data from local files, csv formats, databases, Azure, and web sources, and learn loading, transforming, and preparing data for analysis.
Data Formatting9:42
Data Cleaning16:05
Explore data cleaning techniques in Excel, addressing missing data, outliers, inconsistent and invalid data, duplicates, and data type issues to prepare clean, analyzable datasets.
Detailed Descriptive Analysis10:39
Explore descriptive statistics in Excel, covering mean, mode, median, quartile, percentile, deviation, standard deviation, and variance, using sum if, count ifs, max, and min, via the data analysis tool.
Where is the Option of Data Analysis?2:20
Discover how to enable Excel's data analytics toolkit by activating the Analysis Toolpak VBA add-in through File > Options > Add-ins, then refresh to access the analysis section.
Descriptive Statistics4:15
VLookup8:54
Pivot Table Part 111:35
Pivot Table Part 27:11
Pivot Table Formatting2:55
Correlation6:48
How to Merge multiple data tables?15:51
What is Insert Slicer?4:09
Random Number Generation3:49
Charts and Graphs15:10
Explore how to create and interpret charts and graphs in Excel, including line, scatter, histogram, box, bar, and pie charts, with pivot tables to summarize data for stakeholder insights.
Quiz
What have you achieved from this Excel Section?0:17

Overview of SQL Section2:06
Explore the SQL section of data analysis, covering commands and operators, SQL server interface, import, primary and foreign keys, clauses, group by, joins, unions, subqueries, and aggregate and comparison operators.
Installation of SQL SERVER7:34
Install SQL Server to enable data analysis with SQL, Excel, Python, and Power BI, establishing end-to-end analytics workflows across popular tools.
Discover the Interface4:09
How to Import Data?8:25
SELECT and FROM Clause4:56
SELECT DISTINCT3:49
Logical Operators1:15
Learn how to use logical operators and, or, not, and between in data analysis with Excel, SQL, Python, and Power BI, especially within the where clause to handle complex conditions.
Comparison Operators3:32
Aggregate Functions7:23
Where with Logical Operators ( AND & OR )8:09
WHERE Clause with Logical Operators Part 18:09
WHERE Clause with Logical Operators Part 212:44
Apply the where clause with logical operators such as between, like, and equals to filter data in the city column, using examples like Los Angeles to illustrate ranges and matches.
Quiz
Group By Part 1 with Aggregate Function4:21
Group By Part 2 with Data Type Change11:01
ORDER BY4:31
HAVING10:00
WHERE AND HAVING2:24
Quiz
Primary Vs Foreign Key3:55
JOIN10:43
UNION5:17
SUBQURIES16:22
Introduction to Case Study1:11
Case Study Part 127:34
Case Study Part 219:36
Quiz 3
What have you achieved from this SQL Section ?0:18

Overview of Python Section2:45
Installation of Anaconda ( For PYTHON )3:45
Opening Layout Introduction7:34
What are Data Types in Python?5:24
Variable, Value and Print8:40
Casting and Case Sensitive5:04
What is Module?5:13
Learn what a Python module is, a file containing data and code that can be imported to reuse dictionaries and variables, such as an info dictionary with a name key.
Module Vs Library3:28
Numpy Library8:42
PANDAS Library15:43
Matplotlib Package14:54
Explore the matplotlib library in Python to visualize data with charts and plots. Label axes, add a title, and use legends and markers to compare multiple data series in Jupyter.
Seaborn Library10:33
Learn how to use Seaborn, a matplotlib-based data visualization library, to create scatter, bar, and line plots with numerical and categorical data, and customize axes and titles in Jupyter.
Basics of Python.
How to Import Data in Jupyter Notebook?7:47
What is Data Wrangling?3:02
Data Cleaning Part 17:30
Missing Data15:47
learn how to identify and handle missing data in Python using isna, count missing values with sum, and clean data with dropna, fillna, and moving average.
Outliers11:28
Inconsistent Data3:53
Data Cleaning Part 23:53
Invalid Data1:49
Duplicate Data5:32
Data Types Issues17:17
Learn to identify and fix data type issues in data frames by converting columns to proper types (int, float, date), handling missing values, and validating results with pandas tools.
Data Cleaning Quiz
GroupBy13:16
Explore data analysis in Python: group by to aggregate sales, merge datasets with primary and foreign keys, and cross tabulate multiple variables in a Jupyter notebook.
Merge8:15
Cross-Tab6:42
Cut8:35
Analysis Methods Part 1 (a)14:34
Explore core data analysis methods—mean, median, mode, standard deviation, and variation—using Excel and basic examples; learn how to compute, assess spread, identify outliers, and prepare for Python and Jupyter workflows.
Analysis Methods Part 1 (b)7:02
Analysis Methods Part 210:25
Analysis Methods Part 39:43
Learn how to use pandas in Jupyter Notebook to inspect data with info, summarize numerical data with describe, and explore relationships with correlation across sales and profit.
Unique Methods14:37
Explore essential python data analysis methods, like head, tail, loc and iloc, sort, n unique, shape, dtype, and value_counts, to inspect and summarize datasets.
Methods Quiz
Graphs and Charts8:15
Case Study Part 137:54
Explore case study in Python using Pandas in a Jupyter notebook to merge datasets, group by product, compute max/min sales and profit, and rank by revenue and export classes.
Case Study Part 222:09
What you have achieved from this Section?0:24

Installation of Power BI Desktop4:28
Explore Power BI12:05
How to upload Data from multiple sources12:17
Creation of Dashboard29:26
Understanding the Formatting of Visuals15:04
Learn to create and format visuals in Power BI by selecting chart types, mapping data to axes, and applying formatting options for axes, data labels, borders, tooltips, and titles.
How to Create a Relationship between Data Tables?6:25
Learn to create and edit relationships in Power BI, linking sales order and customer data via customer ID, understand many-to-one cardinality, and apply cross-table filtering in visuals.
Power BI Quiz
Conclusion0:19

Requirements

No programming experience needed. You will learn everything you need to know.
Should be eager to learn.

Description

Data analytics has been one of the fastest-growing fields in the last five years. The use of major tools like Excel, SQL, and Python has elevated its importance, as these tools allow analysts to accurately and professionally uncover the story behind the data.

This course is structured to provide a step-by-step guide to you, starting from the basics of each tool and gradually building up to more advanced concepts. Through hands-on exercises and real-world examples, you will learn how to manipulate data, perform statistical analyses, and create compelling visualizations and dashboards.

In this course, we will cover :

In Excel Section:

Excel functions for data analysis.
Excel fundamental concepts such as Sorting, Filtering, Statistical, and text functions.
Create PivotTable slicers for interactive filtering.
Analyze time-based data with slicers.
Refresh and update data connections.
Combine data from multiple sources.
Perform data analysis on external datasets.
Construct various chart types (bar, line, pie, etc.).
Customize chart elements (titles, axes, data labels).

In SQL Section:

Working with SQL Queries to retrieve data from databases for Analysis.
Understand the concept of Sub-Queries or Inner Queries. Joining tables and combining data from multiple sources.
SQL- DDL, DML, and DQL commands.
Performing data manipulation.
Learn how to apply different conditions to datasets.
Understand the concept of Sub-Queries or Inner Queries.
Discovering these concepts with a Case Study.

In Python Section:

Python's fundamental concepts include Object-oriented programming.
Work with Jupyter Notebooks.
Introduction to the NumPy and the Pandas Library.
Data Cleaning and Handling Missing Values.
Descriptive Statistics.
Correlation Analysis.
Learn about Data Story Telling with Matplotlib and Seaborn.
Hands-on Projects.

In Power BI Section:

Understand the Power BI ecosystem
Install and set up Power BI Desktop
Navigate the Power BI interface
Transforming and cleaning data
Data modeling basics
Creating simple visualizations (tables, charts)
Using filters and slicers
Creating interactive reports and dashboards
Combining multiple data sources
Hands-on projects and real-world applications

So,

You will get to practice the exercises and work on some exciting projects.

Enroll now and make the best use of this course.

Who this course is for:

This course is particularly for those people who want to learn Data related things a student, a teacher, and Corporate sector people included.
Data curious guy who wanted to learn how to gather hidden information by using Excel, SQL, Python, and Power BI.
Students looking for a comprehensive, engaging, and highly interactive approach to learning Data Analysis.
A person who wants to improve the system and change the manual routine works into automatic work.
A Doctor, Teacher, Engineer, or even anyone who belongs to any particular domain can learn about Data Engineering.

Data Analysis by Excel, SQL, Python and Power BI

What you'll learn

Explore related topics

Course content

Data Analytics Foundation, and Connection with other Fields, and Future Trends.7 lectures • 37min

Case Study of Real Life3 lectures • 1min

Data Analysis with EXCEL18 lectures • 2hr 23min

Data Analysis with SQL25 lectures • 3hr 9min

Data Analysis with PYTHON35 lectures • 5hr 32min

Data Visualization with Power BI7 lectures • 1hr 20min

End of THE BIG Course.1 lecture • 1min

Requirements

Description

Who this course is for: