A simple and powerful roadmap you need to follow to become a data analyst ↓

For every project, you will be using a variety of techniques like
        - Data collection
        - Data manipulation
        - Data cleaning
        - Data visualization
This process is referred to as data wrangling.

Now let's take a look at the tools you need to learn to perform each of these techniques:

Data collection
- Online datasets

Data manipulation, Data cleaning
- SQL
- Python (Pandas, NumPy)
- Excel

Data visualization
- Tableau /Power BI,
- Python (Matplotlib, Seaborn, Plotly)

1️⃣  SQL basics:

SELECT, FROM, WHERE, GROUP BY, ORDER BY, HAVING
Manipulation - INSERT, UPDATE, DELETE, ALTER
Aggregate functions - COUNT, SUM, AVG, MIN, MAX
Joining tables - LEFT, RIGHT, INNER, FULL OUTER, SELF JOIN
- Filter data w/ subqueries
- Table views, indexes, constraints

2️⃣  SQL Advanced:

- Window functions
mode.com/sql-tutorial/sql-window-functions

- Data type conversions
mode.com/sql-tutorial/sql-data-types

- Date time functions
mode.com/sql-tutorial/sql-datetime-format

- CTEs (common table expressions)
learnsql.com/blog/what-is-common-table-expression

- Performance tuning
mode.com/sql-tutorial/sql-performance-tuning

3️⃣  Python

While most of the roles do not require Python, knowing it will set you apart and, more significantly, make your job much simpler.
Some useful Python libraries are:

Analysis - pandas, numPy
Visualization - matplotlib, plotly, seaborn
Web scraping - BeautifulSoup

Python data analysis - youtu.be/GPVsHOlRBBI


4️⃣ Excel

As a data analyst, Excel is a powerful tool that can be used to discover in-depth insights into your data and find patterns and trends.

Basics -  youtu.be/TPBS-7OkSnM
Data analysis - youtu.be/3lULeXMt_zg

5️⃣ Excel Advanced concepts:

VLOOKUP, HLOOKUP, MATCH, INDEX
PivotTables
Power Query

Complete Playlist: youtube.com/playlist?list=PLUaB-1hjhk8Hyd5NiPQ9CND82vNodlFF5

6️⃣ Tableau / Power BI

Both Tableau and Power BI are powerful tools for data visualization. Choose one of your choice, or familiarize yourself with both.

I use Tableau Public as its free.
For inspiration: public.tableau.com/app/discover/trending

Tableau concepts you need to know:
- Connecting data
- Dimensions, measures, calculated fields
- Filters and aggregation
- Data mapping, formatting
- Data storytelling

Roadmap to Becoming a Data Analyst

Do you know that you can use your own data to create a unique data analytics project for your portfolio?

🎨 With a bit of creativity, you can come up with projects that are sure to make you stand out.

📊 6 simple but unique data analysis project ideas you can explore today ↓

For each of these projects, you’ll be using:

SQL / Python: to extract and clean the data
Tableau / PowerBI: to visualize the data

1.  💸 Finance Tracker:

Keep track of your spending habits, including what you buy, how much it costs, and how often you buy it.

Identify patterns and trends in your spending, and use this analysis to make informed decisions about your finances.

Idea: public.tableau.com/app/profile/johndavidsant/viz/PersonalFinanceDashboardusingMINTdata/PersonalFinanceDashboard


2.  🧘 Daily Activity Tracker:

Collect and analyze data on your daily activities, such as how much time you spend coding, working, sleeping, exercising, and so on.
Visualize the data using Tableau to gain insights into your daily routines.

3.  📕 Reading Habits:

Track the books you read, including the title, author, and number of pages.
Analyze the data to gain insights into your reading habits, such as the genres you prefer and the average length of books you read.

Idea: public.tableau.com/app/profile/ana7044/viz/MyReadingTracker/Dashboard1

4.  😪 Sleep Tracker:

Track your sleep, including the duration and quality of your sleep.

Analyze the data to gain insights into your sleep habits, such as the impact of stress, caffeine, and exercise on your sleep.

Idea:
poeljenner.medium.com/visualizing-2-years-of-sleep-data-with-fitbit-pandas-and-tableau-f3ee7dfbcd51

5.  ⛹️‍♀️ Fitness Progress:

Track your fitness progress, including the type of exercise, duration, and intensity.

Keep a log of your activity, and review it regularly, to make sure you are staying on track and meeting your fitness goals.

Idea:
gravyanecdote.com/sport-and-fitness/how-to-track-strava-with-tableau/


6.  🍱 Food Tracker:

Track and analyze your food intake, including the type of food, portion size, and calorie count.

Not sure where to begin?

For that, I've already posted a thread on the end-to-end process for exploratory data analysis.

Check it out:

https://twitter.com/eyeshreya/status/1615722788496760832?s=20

That's all for this one.

Hope it helps!

Do you have any similar projects? Share it below, would love to see :)

Project ideas for data analytics portfolio

Step by step process for Exploratory Data Analysis ↓

Before we start, let us first understand what exactly is EDA and why is it important?

Exploratory Data Analysis is an approach to analyzing and understanding data sets through visualizations and statistical techniques.
EDA is an iterative process and can be done multiple times.

📊 It is a crucial step in the data science processes as it allows analysts to:

• Gain a deeper understanding of the data they are working with
• Identify patterns and correlations
• Identify outliers that may need to be addressed before building models or making predictions.

1.  Define the problem 🎲:

The first step in the process is to clearly define the problem you want to investigate. This ensures that you are gathering the necessary data to answer your question.

Gather the relevant data from various sources like databases or spreadsheets.

2.  Clean and organize the data:

It is important to clean, organize the data to ensure it is ready for analysis + makes it easy to work with. This step involves -

- removing any missing/ irrelevant data
- sorting the data
- creating new columns
- merging multiple data sets, etc

3.  Univariate Analysis:

This step involves analyzing variables individually to understand the distribution & identifying outliers.

Statistical measures to describe data:
- mean, median, mode, std. dev.

Viz. to understand the distribution:
- histograms, boxplots, scatterplots

4.  Bivariate Analysis:

🔍 This allows you to analyze the relationship b/w pairs of variables.

- Useful for identifying patterns or correlations in the data. Visualizations such as scatter plots, line plots, and bar charts help you understand the relationship b/w variables.

5.  Multivariate Analysis:

Allows you to analyze the relationship among multiple variables.
- Helps you gain a deeper understanding of the data and identify any trends or patterns.
Techniques such as factor analysis, and cluster analysis can be used.

6.  Data visualization:

Once you're done with data analysis, it's time to be a data wizard and create some visuals to get some deeper insights & communicate your findings. 🧙‍♂️

- Use data viz libraries as Matplotlib, Seaborn, Plotly and Bokeh to create scatter plots & heat maps.

7.  Draw conclusions:

At the end, you should summarize your findings, draw conclusions, and identify any next steps for further analysis or investigation.
This allows you to determine what additional analysis or research is needed to answer your initial question or problem.

That's all for this one! Thanks for reading 👋

Hope it helps.
Planning to post such threads regularly.
Give a follow if you don't want to miss out on new content :)

Exploratory Data Analysis

Hey data analytics learners! 📊

One of the best ways to master a new skill and develop your portfolio is through project-based learning.

6 free virtual project-based internships based on real world scenarios from companies like Deloitte, KPMG, Accenture and Tata ↓

🧠 Why should you consider trying these programs:

- Virtual, self-paced and free
- Based on real life scenarios
- Applied knowledge
- Develop your critical thinking skills
- Learn problem solving approaches

Time to dig in! ⤵️


1.  KPMG Data Analytics Internship

What you’ll learn:
• Data quality and analysis
• Data visualization
• Perform data-driven insights
• Build dashboards

→ theforage.com/virtual-internships/prototype/m7W4GMqeT3bh9Nb2c/Data-Analytics-Virtual-Internship

2.  PwC Switzerland: Power BI in Data Analytics

What you’ll learn:
• Visualize data in Power BI
• Build dashboards
• Drive insights from the data

→ theforage.com/virtual-internships/a87GpgE6tiku7q3gu

3.  Tata Data Visualisation Internship

What you’ll learn:
• Learn business-driven approaches
• Data analysis
• Communicate effective insights

→ theforage.com/virtual-internships/MyXvBcppsW2FkNYCX

4.  Accenture Data Analytics Program

What you’ll learn:
• Data Cleaning, Modeling
• Data Visualization
• Storytelling w/ data

→ theforage.com/fast-track/data-analytics-virtual-experience

5.  Quantium Data Analytics Program

What you’ll learn:
• Data preparation, validation & analysis
• Data wrangling & visualization
• Statistical testing

→ theforage.com/virtual-internships/prototype/NkaC7knWtjSbi6aYv/Data-Analytics

6.  GE Digital Data Analytics

What you’ll learn:
• Data engineering (SQL)
• Visualization, storytelling w/ data
• Business Intelligence

→ theforage.com/virtual-internships/prototype/ThbphD5N5WRsd9Mxo/Digital-Technology-(Data-Analytics)-Virtual-Experience-Program

That's all for this thread.
See you in the next one!

One of the key benefits of project-based learning is that it allows one to apply their knowledge and skills to perform industry-based, meaningful tasks.

Hope it helps! 👋

Data Analytics Portfolio Project

If you're looking to learn Data Analysis in Python, here are some excellent short & concise playlists to follow:

- NumPy
- Pandas
- Matplotlib
- Seaborn
- Plotly
+ Projects!

0. Python:

Want to brush up your python skills right from the fundamentals? This playlist will do the job -

youtube.com/playlist?list=PL-osiE80TeTskrapNbzXhwoFUiLCjGgY7

1. NumPy:

This is a 1-hour video covering concepts like arrays, indexing, math, statistics, reshaping, and more.

youtu.be/QUT1VHiLmmI

2. Pandas:

One of the most widely used library to analyse data, this playlist covers everything you need to know -

youtube.com/playlist?list=PL-osiE80TeTsWmV9i9c58mdDCSskIFdDS

3. Matplotlib:

Now that you know how to analyze data, it's time to create beautiful plots for visualization -

youtube.com/playlist?list=PL-osiE80TeTvipOqomVEeZ1HRrcEvtZB_

4. Seaborn:

Built up on matplotlib, Seaborn takes a level-up and makes it easier to work with dataframes and make more descriptive plots-

youtu.be/6GUZXDef2U0


5. Plotly:

Want to level-up your plotting games and make interactive, animated plots? Learn Plotly -

https://youtu.be/GGL6U0k8WYA

Data Analysis Projects:

Woosh, you now know the data analysis libraries, it's time to make some projects, right?

- Spotify Data Analysis: youtu.be/8d7ywKCm6HI
- Sales analysis with Pandas: youtu.be/eMOA1pPVUc4
- Exploratory Data Analysis Project: youtu.be/kLDTbavcmd0

That's all for this one!

Thanks for reading and give @eyeshreya a follow for more such content :)

Looking for people in the same domain to connect, say hi!

Learn data analysis for free from these YouTube videos

These cheatsheets will make your data visualization journey simpler ↓

Includes:
• Intro to viz
• Matplotlib
• Seaborn
• Bokeh
• Plotly

(links are at the end)

Download them here:

Data Viz - datacamp.com/cheat-sheet/data-viz-cheat-sheet
Matplotlib - datacamp.com/cheat-sheet/matplotlib-cheat-sheet-plotting-in-python
Seaborn - datacamp.com/cheat-sheet/python-seaborn-cheat-sheet
Plotly - images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf
Bokeh - datacamp.com/cheat-sheet/python-data-visualization-bokeh-cheat-sheet

These cheat sheets have helped me save a lot of time, hope they help you too, thanks to @DataCamp!

That’s all for this one! 👋


You can read the unrolled version of this thread here: https://typefully.com/eyeshreya/0xatCaq

Data visualization cheat sheets to save your time

My top 3 preferences to learn SQL for free:

1. MySQL Tutorial for Beginners by @moshhamedani

This 3-hour course helps you understand SQL and write queries in no time!

🔗 https://youtu.be/7S_tz1z_5bA

2. W3Schools SQL

If I need to revise a concept, this is my go-to resource every time.

🔗 https://www.w3schools.com/sql/

3. SQL Bolt

Helped me a lot when I was learning the language for the first time, the interactive lessons help you quickly learn SQL right in your browser.

🔗 https://sqlbolt.com/

4. SQL Practice

Practice by solving queries on this website.

🔗 https://www.sql-practice.com/

5. DataLemur by @NickSinghTech

Found this one last month, extremely helpful resource!
Practice SQL interview questions asked by tech companies.

🔗 https://datalemur.com?referralCode=LKnDX1JS

6. SQL Study Plan by Leetcode:

What could be better for you to follow than a set scheduled plan? Well, this one includes a 30-day strategy for practicing SQL queries daily!

🔗 https://leetcode.com/study-plan/sql/

That's all for this one!
Do you have any resources? Would love to explore!

See y'all soon. Till then, explore some data ;)
@eyeshreya

Learn SQL for free

Technical Writing 101

Technical writing is the process of creating documentation that is simple enough for both technical and non-technical readers to grasp.
If you have the right tools and resources, the journey can made be a little easier and can save you tonnes of time:

🧵↓

✅ Challenges To Commit:

- Write 1 article/ week.
- Write 500 words/ day.
- At least 4-5 blogs/ month.

Consider joining public challenges like #2articles1week by Hashnode. It will give you a push to write as you're committing in public:

https://hashnode.com/@eyeshreya/joinme

🧠 Learn:

Learn the skill for free from these courses -

1. Coursera's tech writing course:
🔗 coursera.org/learn/technical-writing

2. Google's Technical Writing Course:
🔗 developers.google.com/tech-writing

🧩 Building Your Portfolio:

If you've a great portfolio (your proof of work), it can land you many paid writing opportunities as well.

Publish for free on these sites:
1. Your blog (hosted on GitHub)
2. Hashnode
3. dev .to
4. Medium

💵 Paid Writing Opportunities:

This is the part where people are interested the most.
However, as I already mentioned, you should first maintain a writing portfolio as they will look for articles you have published and may approach you if things go well.

Find paid writing gigs:


1. Who Pays Technical Writers:
whopaystechnicalwriters.com

2. Paid Community Writer Programs: github.com/tigthor/PaidCommunityWriterPrograms

⚒️ Tools:

Having the right tools can help you in the process and save your time as well.
Here are some of the tools I use the most -

1. Hemmingway Editor: hemingwayapp.com
2. Grammarly Chrome Extension: app.grammarly.com
3. Quillbot AI Tool: quillbot.com

📷 Cover Images:

To produce attractive articles, you need beautiful covers as well, right?

- Canva: canva.com
- Figma: figma.com
- CoverView: coverview.vercel.app (one of my favorites)


📽️ YouTube Channels:
🔗 Amruta Ranade: youtube.com/c/AmrutaRanade

✍️ Creators to follow:
@The_GreatBonnie
@DailyDevTips1
@lo_victoria2666
@catalinmpit

@tapasadhikary

Finally, keep in mind that you are doing this for yourself, so avoid viewing it as a liability. Take your time and don't rush it.

Don't stress about completion of goals; something is always better than nothing!
Thanks for reading. Hope it helped you!

Follow @eyeshreya for more!

Building projects is a great approach to stand out from the crowd and expand your data analytics portfolio. 📈

🚀 8 free datasets for your next data science and analysis projects.

↓

1. Google Dataset Search

🔗  https://datasetsearch.research.google.com/


2. Kaggle datasets

🔗  https://www.kaggle.com/datasets


3. Datahub

🔗 https://datahub.io/collections


5. UCI Machine Learning Repository

🔗  https://archive.ics.uci.edu/ml/datasets.php


6. FBI Crime Data Explorer

🔗  https://crime-data-explorer.fr.cloud.gov/


7. Global Health Observatory Data Repository

🔗  https://apps.who.int/gho/data/node.home


8. IMDB Movie Dataset

🔗  https://www.imdb.com/interfaces/

Free datasets for data analysis projects

11 YouTube channels that can help you accelerate your Data Analytics journey 📊

↓

1. Ali Ahmad

- Career advice
- Interviews
- Portfolio projects

https://www.youtube.com/c/AliAhmad1987

2. Ankit Bansal

- SQL Interview problems ⭐
- Interview preparation

https://www.youtube.com/user/1988ankitbansal

3. Corey Schafer

- Excellent Python tutorials
- Playlists on Pandas, Matpotlib, SQL

https://www.youtube.com/c/Coreyms

4. Data Interview Pro

- Interviews with data analysts
- Interview prep

https://www.youtube.com/channel/UCAWsBMQY4KSuOuGODki-l7A

5. Jovian

- Data analysis playlist
- Project walkthroughs

https://www.youtube.com/channel/UCmKaoNn0OvxVAe7f_8sXYNQ

6. Learnit Training

- Excel and PowerBI tutorials

https://www.youtube.com/c/LearnitTraining

7. Luke Barousse

- Tips and tricks
- Exploring the data field

 https://www.youtube.com/channel/UCLLw7jmFsvfIVaUFsLs8mlQ

8. Shashank Kalanithi 

- Tutorials

Do watch his "Day in the life of a Data Analyst" series. It helped me A LOT.

https://www.youtube.com/user/95shawshankk19

9. StrataScratch
 
- Interview questions
- Project guidance
- Talks

https://www.youtube.com/channel/UCW8Ews7tdKKkBT6GdtQaXvQ

10. techTFQ

- SQL Practice ⭐

https://www.youtube.com/c/techTFQ

11. Tina Huang

- Exploring the data field
- SQL Sundays series ⭐

https://www.youtube.com/channel/UC2UXDak6o7rBm23k3Vv5dww

🍿 TIPS: 

- These are in no particular order.

Try to explore their playlists for a better overview
Analyse the playlists contents to help you decide which is good for you.


⭐ Don't fall into tutorial hell. Pick one and stick with one, then pick another if you feel incomplete.