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If you’re looking to break into data analytics, you’ve probably already looked at a few data scientist job postings. You’ve probably also seen several tools and programming languages listed in the required skill set: SQL, Excel, Power BI, Tableau, Python, and more.
Well, you can enroll in multiple courses to learn each of these skills. But wouldn’t it be much better if you could go through one comprehensive bootcamp that helps you learn all of these skills as well as build a portfolio of projects?
Completely free Data analytics bootcamp for beginners by Alex Analyst is what you’re looking for to start your career as a data scientist. In addition to learning SQL, Excel, Power BI, Tableau, and Python, you’ll also create projects, learn how to write a CV, and much more. Now let’s get into the content of this bootcamp.
To combine: Data Analytics Bootcamp for Beginners (SQL, Tableau, Power BI, Python, Excel, Pandas, Projects and more)
The course starts with a high-level roadmap on how to become a data scientist and then goes into detail about each of the required tools, the first of which is SQL.
This SQL section of the tutorial is divided into three parts: Basics, Intermediate SQL, and Advanced SQL.
The Basic SQL section includes:
- Select + From instructions
- Where is the statement
- Group by and order by
The intermediate portion of the SQL tutorial covers the following topics:
- Internal and external joins
- Relationships
- Statement of the case
- Having a clause
- Updating and deleting data
- Aliasing
- Breakdown by
The Advanced SQL section will teach you:
- Common table expressions (CTE)
- Momentary tables
- String functions
- Stored procedures
- Subqueries
The module concludes with several portfolio projects on data mining and cleansing using SQL.
As a data scientist, you shouldn’t be surprised if all your work involves crunching numbers on spreadsheets. Once you have learned the basics of SQL, which you can improve with practice, you can start learning Excel.
Almost all organizations operate Excel or a similar spreadsheet tool, so learning how to work with them is very helpful.
The Excel section covers the following topics:
- Pivot tables
- Formulas
- XSEARCH
- Conditional formatting
- Charts
- Cleaning data
Similar to the SQL section, you will be able to work on a full-blown data science project using Excel.
Now that you have a good understanding of SQL and Excel, which should be enough for almost any basic data analysis, it’s time to move on to learning BI tools.
The Tableau tutorial section starts with installing Tableau and covers the following topics:
- Creating your first visualization
- Using calculated fields and bins
- Using joins
Then you’ll work on a beginner-friendly project.
The Power BI section walks you through using Microsoft Power BI for data analysis and visualization, starting with installing Power BI.
Here’s an overview of what this section covers:
- Creating your first visualization
- Using a power query
- Creating and managing relationships
- Using DAX in Power BI
- Using drill down
- Conditional formatting and lists
- Popular visualizations in Power BI
Similar to the previous sections, you can also work on a guided project in this section of Power BI.
Now that you’re familiar with most of the tools used in data science, it’s time to learn the most commonly used data programming language. That is, Python.
This section covers Python and data analysis using Pandas, with the opportunity to work on elementary projects. Topics covered include: Python Basics, which covers the basics of Python and several projects to which you can apply what you learn. Then you will learn how to scrape web pages in Python.
The pandas tutorial covers the following topics:
- Reading files
- Filtering columns and rows
- Indexes
- Grouping and aggregation functions
- Combining data frames
- Creating visualizations with pandas
- Data cleansing
- Exploratory Data Analysis (EDA)
You can then work on two portfolio projects on working with APIs and web scraping.
At this point, you’ve learned all the skills you need to become a data scientist, and you’ve also worked on projects to add to your portfolio. So what’s next? It involves applying for jobs, going through interviews, and getting the job.
The final part of the data scientist boot camp offers useful career advice on the job search process:
- How to create a portfolio website
- How to create a good data analyst CV
- Tips for using LinkedIn to find a job
This is really helpful because very few courses cover this aspect of what you should do After you learned the required skills and construction projects.
I hope you found this extensive review of this bootcamp helpful. So what are you waiting for? Go ahead and start learning today.
Elated learning and coding!
Bala Priya C is a software developer and technical writer from India. He likes working at the intersection of mathematics, programming, data analytics and content creation. Her areas of interest and specialization include DevOps, data analytics and natural language processing. She likes reading, writing, coding and coffee! He is currently working on learning and sharing his knowledge with the developer community by writing tutorials, guides, reviews, and more. Bala also creates compelling resource overviews and coding tutorials.