By Brittany City

Are you interested in learning Data Analytics but not sure where to start? If so, then this article is for you.

As a data analytics instructor for over 2 years, I am often asked 2 questions: “what skills should I learn to become a data analyst?” and “what resources or courses are the best for learning those skills?”. Here’s the short answer for them:

  1. The key skills to become a data analyst are: Excel, Power BI, Tableau, SQL, and Python.
  2. In this article, I will be providing some of my favorite resources that I used on my data journey and use with my students. However, there are millions of courses online (free or paid) the specific course you decide to take does not matter instead focus on completing at least 2-3 projects utilizing each skill.


My goal for this article is to provide the long answer through a Day-by-Day plan for learning data analytics skills with resources and project ideas.

Warning: This guide to does not include any certifications. There has been a wave of misleading information that certifications are needed or required to transition to data roles. My guide in this article focuses on skill building, projects, and networking.

Disclaimer: This guide has been broken down into 100 days because that is how long I instruct a full Data Analytics course at technology schools and boot-camps. Feel free to modify the timeline as needed.

No alt text provided for this image


Three major advantages formal education provides over self-learning is structure, networking, and accountability. I’m providing the structure in this article but networking and accountability are on you.

Pre-Work: Networking

I know we hate the word, but networking and social media are truly what’s making the world go around these days.

  1. Setup a LinkedIn and Twitter account before starting their tech learning journey. The profiles don’t need anything fancy, just a clear photo and headline of who you are. I recommend using a headline/bio such as “Student Data Analyst”. You will use these accounts during the next 100 Days to share your projects and thoughts along your journey. After you complete this guide, you will use these accounts to find a job!
  • LinkedIn: set a daily connections goal with the main goal to reach 500 connections. I don’t know what it is, but you unlock some next level LinkedIn at 500 connections. Search “data analyst”, “business intelligence analyst”, etc. to find connections. You can send a quick sentence explaining why you want to connect, but to be honest I don’t always do that. I also recommend joining a tech or data group where you can post questions, ask for project feedback, and connect with people.
  • Twitter: follow tech content creators and hashtags such as #blacktechtwitter, #blackindata, etc.
  • Optional: There are also virtual tech communities on Facebook, Clubhouse, and Discord. You can find in-person networking events in your area on Meetup.


Pre-Work: Schedule

Whoever said “a goal without a plan is just a wish” had to be a Virgo. I think all goals require monthly, weekly, and daily planning but that’s a whole other article. In order for you to learn the skills at the end of the 100 days (or even the end of the year), you need a plan.

  1. Evaluate your current life schedule and identify when you have time for learning. I created this weekly template to help with this exercise. I recommend 10-12 hours per week in order to complete in 100 days.
No alt text provided for this image


Optional Pre-Work: Subscribe to DataCamp

During my data science boot-camp, we were given assignments in DataCamp to learn the topics and really used in-class time to work on projects and ask questions. I highly recommend DataCamp to anyone interested in any data career. The courses provide reading, lecture videos, and hands-on exercises to help learn and apply the skills to real world problems.

The Basic plan is free and offers the first chapter of each course and 6 full courses. The Premium plan is $300/yearly or $39/month and offers Tableau and PowerBI (cannot access with Basic plan). You can cancel at any time. I am not affiliated with DataCamp or paid for this message.

Day 1-5: Introduction to Data & Statistics

Do you have to be good in math to work in tech? No. Do you have to be “okay” in statistics to work in data? Yes. My data journey includes a Udacity Nanodegree, local boot-camp, and two master’s degree programs and they all began with course(s) in statistics. If any of you are interested in data science, it is even more critical for you to learn and grasp concepts.

Must know statistical concepts for data analytics are:

  • Type of graphs/plots and when to use them
  • Population and Sample
  • Normal Distribution
  • Central Tendency
  • Variance and Standard Deviation
  • Covariance and Correlation
  • Central Limit Theorem
  • P-value
  • Probability


This Statistics course by freeCodeCamp covers all of the must know statistical concepts. The video description includes time stamps so you can hop around topics as needed.

Day 6-20: Microsoft Excel

Most of you are probably familiar with Excel and may have even played with it for school or personal use. Excel can be a very powerful tool and is still used very commonly (if not daily) by data analysts.

As a data analyst, you will use Excel to

  • convert data types including text, time, and dates
  • create formulas, functions, and conditional aggregations
  • implement VLOOKUP
  • use Pivot Tables
  • create data visualizations and dashboards
  • and more!


This Microsoft Excel Tutorial for Beginners Course by freeCodeCamp teaches Excel techniques through 6 real-world projects.


MyOnlineTrainingHub provides a great walk-through demo of interactive Excel dashboards and you can also access the data and follow along.


DataCamp: Data Analysis in Excel course

Excel Project Ideas:

  1. Import a txt (.txt or .csv) file and clean using the Import Wizard
  2. Import a txt (.txt or .csv) file and clean without using the Import Wizard
  3. Clean any messy data set by using techniques such as getting rid of spaces, covert numbers to text, remove duplicates, change text to the same case, find and replace
  4. Create a dashboard to answer business questions and/or tell a story


Day 21-35: Power BI

Power BI is another Microsoft product so it will look and feel similar to Excel which hopefully help you ease into it. Power BI is a very common tool for data visualizations and can also be used for data cleaning.

Here is where you can download Power BI, but unfortunately it currently can only be downloaded on PC.

Edureka’s full course on Power BI has over 2.5 million views! The course provides a great “lay of the land” and what everything does.


MyOnlineTrainingHub offers a start to finish Power BI demo with the data files available to follow along. You can even follow along with another dataset and BOOM project!


I highly recommend using DataCamp if you have a Mac (that’s what I did): Intro to Power BIData Visualization in Power BIDAX in Power BI.

You can find even more hands-on practice on WiseOwl.

PowerBI Project Ideas:

  1. Create a dashboard at least 4 visualization (always have a title) and utilizing slicer filters. Tell a complete story and/or answer business question(s).
  2. Create a dashboard with a map with drill-down features.
  3. Create a dashboard with time series analysis and cards to display KPIs.
  4. Connect a web data source to the dashboard and visualize.
  5. Use Query Editor to rename columns and join tables then create dashboard.


Day 36-50: Tableau

Tableau is another data visualization software with similar functionalities as Power BI. You can find debates of which one is better on Youtube and Twitter. I personally prefer Tableau but solely for aesthetic reasons, it feels more Apple/iOS like with its sleekness. And I can also download it on my Mac.

  1. Download Tableau Public


Edureka offers a full course on Tableau taking you from the UI walkthrough to building charts and dashboards to functions and calculations.


freeCodeCamp also have an in-depth Tableau course but it may be redundant to Edureka so pick one of those or hop as needed.


Alex The Analyst has tons of Tableau content (all Tableau videos) and in this video he’s walking through a full Tableau project.


DataCamp courses: Intro to TableauAnalyzing Data in Tableau

Project Ideas:

  1. Create a Tableau Resume (checkout other resumes)
  2. Create a Tableau Story using multiple dashboards and providing an introduction and data dictionary dashboard
  3. Import multiple datasets and apply features to join the tables (for example, merge state columns in two datasets to bring in geographical data to create a map)
  4. Complete a Cluster Analysis in Tableau (DataCamp tutorial)
  5. Apply the same PowerBI project ideas above


Day 51-72: SQL

SQL is the most important, we can’t analyze or visualize data without being able to extract it from databases. I recommend digging the deepest in SQL by taking intro and intermediate courses.

  1. Install MySQL Server and Workbench on Windows 10
  2. Install MySQL Server on Mac OS
  3. Install MySQL Workbench on Mac OS (done separately for Mac users)


freeCodeCamp is always coming through! They have a full course on SQL and they’re using MySQL Relational Database Management System (RDBMS). They give a great intro into databases and all that too.


DataCamp courses: Intro to SQLJoining Data in SQLIntermediate SQLRelational Databases in SQL.

W3Schools is an amazing supplemental resource for SQL with the best query definitions and examples.

Practice Exercises & Project Ideas:

  1. Company Database Querying Exercise – After answering the questions, I recommend uploading your SQL files to GitHub and sharing the link on LinkedIn.
  2. Use MySQL’s Table Data Import Wizard (video) to create a database from a series of csv files. You may need to tweak the files beforehand to make sure they have matching keys. Then complete a series of queries to answer business question(s) and BOOM project!


Day 73-100: Python

There are still tons of data analysts that never use Python or any other programming language to complete their work. This is an optional section truly but will help you stand out from other applicants or prep you for roles that really require it. If you’re interested in finance or forecasting Python can be very beneficial as well.

  1. Install Anaconda


Edureka has a quick Python, Anaconda, and Jupyter notebook tutorial for beginners.


freeCodeCamp has a full course on Python for beginners including installation, data types, variables, lists, tuples, functions, if statements, loops, and reading files. I will note this is a general Python course as Python can be used for tons of other fields outside of data analytics.


Edureka has a great video on Exploratory Data Analysis (EDA) that you can follow along with your own dataset and BOOM project!


I highly recommend taking the DataCamp courses for Python as they are very specific to using the language for data.

DataCamp Courses:

  1. Intro to Python
  2. Intermediate Python
  3. Time Series in Python


Python Project Ideas:

  1. Data Exploration: import dataset, clean data, and create real-world business questions to answer by exploring the data using calculations and visualizations (example)
  2. Forecasting Stock Prices (DataCamp walk through)


No alt text provided for this image


Last Notes:

  • Data is everywhere to play with!
  • If you have a preferred industry or domain (finance, healthcare, sports, etc.), I would recommend using data related to this for your projects. Doesn’t have to be all but a good amount to tailor your resume when that time comes. ProjectPro provides project ideas for Power BI and Tableau broken down by industry.
  • Connect with me on social media! Let me know if you have any questions and tag me when you share any part of this journey.


The post A COMPLETE GUIDE TO LEARNING DATA ANALYTICS IN 100 DAYS appeared first on Black In Data.