Become a Data Analyst - (ETL, SQL, Power BI, Python,R )

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Bluelime Learning Solutions

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1. Introduction to Data Analysis

1. Introduction.html

2. What is Data Analysis.html

3. Roles and Responsibilities of a Data Analyst.html

4. Introduction to ETL SQL Power BI Python R.html

5. Tools and Technologies used in Data Analysis.html

2. SQL for Data Analysis

1. Introduction to SQL.html

2. MySQL Database Download for Windows.mp4

14:55

3. MySQL Database Download for Mac.mp4

06:58

4. Introduction to MySQL Workbench.mp4

06:04

5. Installing MySQL Workbench for Mac.mp4

05:36

6. Basic database concepts.mp4

09:00

7. What is a Schema.mp4

02:50

8. Database Schemas.mp4

04:50

9. MySQL Data Types.mp4

06:56

10. SQL basics (Select Where From).mp4

06:24

11. What are SQL Joins.html

12. INNER JOIN.mp4

11:57

13. LEFT JOIN.mp4

06:05

14. RIGHT JOIN.mp4

04:13

15. SELF JOIN.mp4

08:07

16. SQL Group By.mp4

03:56

17. SQL Having.mp4

06:31

18. SQL Order By.mp4

06:10

19. SQL Distinct.mp4

05:57

20. SQL Character Function.mp4

01:59

21. SQL Concat Function.mp4

02:42

22. Introduction to Aggregate Functions.mp4

02:05

23. AVG Function.mp4

03:29

24. COUNT Function.mp4

08:49

25. SUM Function.mp4

04:22

26. MIN Function.mp4

02:25

27. MAX Function.mp4

02:48

28. Introduction to some SQL SQL keywords.html

29. SQL BETWEEN.mp4

06:39

30. SQL IN.mp4

08:39

31. SQL LIKE.mp4

10:59

32. SQL UNION.mp4

08:05

33. SQL Subquery.mp4

04:37

34. Nested Sub query.mp4

03:22

3. ETL (Extract Transform Load) Processes

1. Introduction to ETL.html

2. ETL Tools.html

3. Extracting Data.html

4. Transforming Data.html

5. Loading Data.html

6. ETL Best Practices.html

4. Setting Up Environment for ETL

1. What is SQL Server.mp4

02:57

2. SQL Server Version.mp4

04:45

3. Note on Software versions.html

4. SQL Server Download.mp4

04:31

5. SQL Server Installation.mp4

09:43

6. Install SQL Server Management studio -SSMS.mp4

05:48

7. Connect SSMS to SQL Server.mp4

02:16

8. Restore sample database.mp4

06:28

9. Restore sample data warehouse database.mp4

05:49

10. What is Visual Studio.mp4

04:32

11. Install Visual studio.mp4

06:46

12. Visual studio workloads.mp4

03:59

13. Install SQL Server Data Tools - SSDT.mp4

04:09

14. Install SSDT Extensions.mp4

06:21

5. Implementing and ETL Process with SSIS

1. Introduction to SSIS.mp4

03:25

2. Create a new SSIS Project.mp4

02:13

3.1 SampleCurrencyData.txt

3. Add and configure flat file connection manager.mp4

06:26

4. Remap column data types.mp4

11:12

5. Add and configure OLE DB Connection manager.mp4

03:39

6. Add a data flow task.mp4

05:02

7. Add and configure flat file source.mp4

04:28

8.1 lookupsqlquery.txt

8. Add and configure lookup transformation.mp4

11:29

9. Add and configure lookup DateKey Transformation.mp4

09:28

10. Add and configure OLE DB Destination.mp4

07:53

11. Test and run SSIS Package.mp4

02:02

6. Python Setup

1. Introduction to Python.html

2. What is Jupyter Notebook.mp4

01:20

3. Install Python on Windows.mp4

03:38

4. Install Python on Mac.mp4

02:54

5. Install Jupyter Notebook with Anaconda.mp4

06:45

6. Running Jupyter Notebook.mp4

08:44

7. Some Jupyter Notebook Commands.mp4

07:28

8. Jupyter Notebook components.mp4

04:26

9. Jupyter Notebook dashboard.mp4

04:20

10. Jupyter Notebook Interface.mp4

05:44

11. Creating a new Notebook.mp4

06:45

7. Python Basics

1. Python Expressions.mp4

03:37

2. Python Statements.mp4

04:42

3. Python Code Comments.mp4

04:48

4. Python Data Types.mp4

04:52

5. Casting Data Types.mp4

02:57

6. Python Variables.mp4

07:28

7. Python List.mp4

09:33

8. Python Tuples.mp4

07:11

9. Python Dictionaries.mp4

10:17

10. Python Operators.mp4

14:55

11. Python Conditional Statements.mp4

08:03

12. Python Loops.mp4

09:04

13. Python Functions.mp4

07:59

8. Data Analysis and Data Visualization with Python

1. Introduction to Python for Data Analysis (Pandas).html

2. Introduction to Python for Data Visualization (Matplotlib Seaborn).html

3. The dataset.mp4

03:06

4.1 us baby names.zip

4. Tabular Data.mp4

08:00

5. Exploring Pandas DataFrame.mp4

02:20

6. Manipulating Pandas DataFrame.mp4

12:57

7. What is data cleaning.mp4

03:28

8.1 crime boston.zip

8. Perform Data cleaning.mp4

22:42

9.1 listings.zip

9. What is data visualization.mp4

02:35

10. Visualizing qualitative data.mp4

12:00

11. Visualizing quantitative data.mp4

13:49

9. Introduction to R

1. What is R.html

2. Installing R for windows.mp4

02:01

3.1 Download+and+install+R+on+Mac.pdf

3. Installing R for Mac.html

4. What is R Studio.html

5. Installing R Studio on Windows.mp4

01:50

6.1 Download+and+install+R+Studio.pdf

6. Installing R Studio on mac.html

7. Exploring R Studio default interface.mp4

09:32

8. Creating a new project in R Studio.mp4

05:34

9. What are Packages.html

10. How to install Packages.mp4

04:19

11. Datasets vs Data Frames in R.html

12. Loading Packages.mp4

06:38

10. Data Analysis and Visualization with R

1.1 sydneybeaches.csv

1. Importing data into R Studio.mp4

05:44

2. How to read data in a csv file with R.mp4

06:28

3. Installing the Janitor Package.mp4

03:30

4. Selecting a subset of the data.mp4

04:53

5. Performing multiple operations using Pipe Operator.mp4

09:36

6. Cleaning columns.mp4

11:18

7. Creating new columns from existing columns.mp4

10:00

8. Create a new project.html

9.1 chocolate.csv

9. Load data into new project.mp4

06:08

10. What is Data Wrangling.html

11. Data Wrangling Steps.html

12. Importance of Data Wrangling.html

13. Perform Data Wrangling.mp4

12:25

14. Create a Scatter Plot.mp4

06:37

15. Create a bar graph.mp4

08:39

11. Introduction to Power BI

1. What is Power BI.html

2. Setup Microsoft 365.mp4

03:43

3. Getting started with Microsoft 365.mp4

04:04

4. Adding user to Microsoft 365.mp4

02:54

5. Installing Power BI Desktop.mp4

02:55

6. Exploring Power BI Interface.mp4

05:02

7.1 Financial+Sample.xlsx

7. Power BI Overview - Part 1.mp4

04:24

8. Power BI Overview - Part 2.mp4

04:05

9. Power BI Overview - Part 3.mp4

04:49

10. Components of Power BI.mp4

02:57

11. Exploring Power BI Service.mp4

04:28

12. Data Analysis and Visualization with Power BI

1. Connect to web based data.mp4

05:07

2. Clean and transform data - Part 1.mp4

07:12

3. Clean and transform data - Part 2.mp4

09:45

4. Combining data sources.mp4

07:59

5. Creating Visualizations Part 1.mp4

06:09

6. Creating Visualizations Part 2.mp4

05:23

7. Publishing Reports to Power BI Service.mp4

04:47

Description

Data Analysis Unleashed: ETL, SQL, Power BI, Python, Jupyter Notebook,Pandas and R for Impactful Business Decisions

What You'll Learn?

The basic principles of data analysis and its importance in decision-making.
The roles and responsibilities of a data analyst.
Understanding the concept of ETL (Extract, Transform, Load) processes.
How to extract data from different data sources.
Techniques to clean and transform raw data for analysis.
How to load transformed data into an appropriate data storage system.
Best practices for ETL processes.
SQL fundamentals for retrieving and manipulating data in relational databases.
Advanced SQL concepts, including subqueries and joins.
Utilizing SQL clauses such as Between, IN, LIKE, and UNION.
Python basics, including data types, variables, and control flow.
Data manipulation in Python using Pandas.
Basic data visualization techniques in Python using libraries like Matplotlib and Seaborn.
Basic data manipulation and analysis techniques in R.
Understanding the concept of Power BI and its role in data analysis.
Transforming and shaping data to fit the needs of your analysis using Power BI.
Creating various data visualizations using Power BI.

Who is this for?

Beginners in Data Analysis: Individuals who are just starting out and want to learn the basics of data analysis. This course starts from scratch, introducing the fundamental concepts before moving on to more complex topics.

Career Switchers: Professionals from non-data-oriented fields who are looking to switch their careers and break into the data analysis industry. This course provides a comprehensive understanding of the required tools and techniques.

Current Data Analysts: Existing data analysts who want to consolidate their knowledge and learn new tools and techniques. This course covers a wide range of tools used in the industry.

Students: College or university students who are studying a related field and wish to enhance their practical skills and knowledge to prepare for a career in data analysis.

Professionals Who Work With Data: Individuals who work with data in their current roles and want to enhance their data analysis skills. This could include roles in marketing, finance, product management, and more.

Aspiring Data Scientists: Those planning to become data scientists in the future. This course can be a stepping stone as it covers Python and R, two programming languages commonly used in data science.

Anyone interested in Data: Lastly, anyone who is curious about data analysis and wants to understand how to turn raw data into actionable insights. This course does not require any prerequisites, making it suitable for anyone with a keen interest in the field.

What You Need to Know?

Basic Computer Skills: Students should be comfortable with basic computer operations, such as downloading and installing software, browsing the internet, and managing files and directories.

Fundamental Understanding of Mathematics: While the course does not delve into advanced mathematics, having a foundational understanding of basic mathematical concepts (like mean, median, mode, percentage, etc.) would be beneficial for comprehending data analysis concepts.

Logical Thinking: Data analysis involves a significant amount of problem-solving and logical reasoning. So, having an inclination towards logical thinking and problem-solving would be beneficial.

Basic Programming Knowledge (Not Mandatory): Prior experience with any programming language would be helpful but is not required. We will cover the necessary programming concepts as we delve into Python and R.

An Enthusiasm to Learn: Data analysis is a vast field with a variety of tools and techniques. Being keen and open to learning new things would definitely be a plus.

Access to a Computer: As the course involves hands-on exercises and practice, having access to a computer where you can install and use tools like SQL, Python, R, and Power BI is required. Please note, some of these software may have system requirements, so ensure your computer meets those.

Remember, this course is designed to take you from beginner to proficient, so don't worry if you're not familiar with some of the concepts or tools we'll be covering. We'll walk through everything you need to know, step by step, to ensure you're comfortable and understand each topic.

More details

Description
Are you intrigued by the world of data and aspire to become a proficient data analyst? Do you wish to master the essential tools and technologies used in data analysis? If yes, then this course, "Mastering Data Analysis: A Comprehensive Guide to ETL, SQL, Power BI, Python, and R" is for you!
The ever-growing expanse of the digital universe has amplified the importance of data analysis across industries worldwide. Businesses, government agencies, and nonprofits are increasingly leveraging data to make strategic decisions, drive operational efficiency, and innovate. Thus, equipping yourself with data analysis skills will not only increase your employability but also provide a platform to significantly impact decision-making processes.
Our comprehensive course starts from the fundamentals of data analysis, moving gradually towards more complex concepts and tools. We firmly believe in the power of practical learning, so the course is replete with real-life examples, hands-on exercises, and case studies to ensure you can apply the concepts you learn in real-world scenarios.
In the initial modules, you'll gain a broad understanding of data analysis, its applications, and the crucial role of a data analyst. You will also be introduced to ETL (Extract, Transform, Load) processes, the backbone of any data-driven operation, learning how data is collected, cleaned, and stored.
Subsequently, we delve into SQL, the language of databases. You'll learn to extract and manipulate data stored in relational databases, starting from simple queries to more advanced topics like subqueries and joins.
The following section takes you through Python, a versatile language extensively used in data analysis. You'll get hands-on experience with libraries like Pandas for data manipulation, and Matplotlib and Seaborn for data visualization.
Next, we introduce R, a powerful language designed specifically for statistical analysis and data visualization. You'll learn about data structures in R and its various applications, enabling you to handle and analyze complex datasets.
Finally, we explore Power BI, Microsoft's flagship business analytics tool. You'll learn to create dashboards and reports, providing interactive visualizations that efficiently communicate your findings.
By the end of this course, you will have developed a robust foundation in data analysis. You'll be well-versed in various tools and technologies, from SQL and ETL processes to Python, R, and Power BI. Most importantly, you'll be equipped with the skill to transform raw data into actionable insights, a vital ability in today's data-driven world.
This course is suited for beginners with no prior experience, as well as those who wish to consolidate their knowledge in data analysis. Whether you're a student, a working professional, or someone curious about the field, this course provides a comprehensive and practical approach to learning data analysis.
Enroll in "Mastering Data Analysis: A Comprehensive Guide to ETL, SQL, Power BI, Python, and R" today and step confidently into the world of data analysis. Your journey towards becoming a skilled data analyst starts here.

Who this course is for:
Beginners in Data Analysis: Individuals who are just starting out and want to learn the basics of data analysis. This course starts from scratch, introducing the fundamental concepts before moving on to more complex topics.
Career Switchers: Professionals from non-data-oriented fields who are looking to switch their careers and break into the data analysis industry. This course provides a comprehensive understanding of the required tools and techniques.
Current Data Analysts: Existing data analysts who want to consolidate their knowledge and learn new tools and techniques. This course covers a wide range of tools used in the industry.
Students: College or university students who are studying a related field and wish to enhance their practical skills and knowledge to prepare for a career in data analysis.
Professionals Who Work With Data: Individuals who work with data in their current roles and want to enhance their data analysis skills. This could include roles in marketing, finance, product management, and more.
Aspiring Data Scientists: Those planning to become data scientists in the future. This course can be a stepping stone as it covers Python and R, two programming languages commonly used in data science.
Anyone interested in Data: Lastly, anyone who is curious about data analysis and wants to understand how to turn raw data into actionable insights. This course does not require any prerequisites, making it suitable for anyone with a keen interest in the field.

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Bluelime is UK based and creates quality easy to understand eLearning solutions .All our courses are 100% video based. We teach hands –on- examples that teach real life skills .Bluelime has engaged in various types of projects for fortune 500 companies and understands what is required to prepare students with the relevant skills they need.

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