Comprehensive Data Analytics Roadmap for 2023 With Free Resources
With vast amounts of data being generated every day, the ability to extract valuable insights from this data has become essential for businesses, organizations, and individuals alike.
In this article, we present a comprehensive data analytics study roadmap for 2023, complete with a curated selection of free resources to aid your learning. From the fundamentals to advanced techniques, this roadmap covers the key aspects of data analytics and provides you with the necessary knowledge and tools to excel in the field.
By following this comprehensive data analytics study roadmap, you’ll gain a solid foundation in data analytics principles and acquire hands-on experience with essential tools and technologies. Additionally, the curated list of free resources will serve as valuable references, tutorials, and practice materials throughout your learning journey. Get ready to unlock the power of data analytics in 2023 and beyond!
Table of Contents:
Introduction to Data
Mastering SQL
Mastering Excel
Python Basics
Python for Data Analytics
Statistics Basics for Data Analytics
Mastering A / B Testing
Introduction to Machine Learning
Power BI for Data Analytics
Build Your Portfolio
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The best interactive roadmaps for Data Science roles. With links to free learning resources. Start here: https://aigents.co/learn/roadmaps/intro
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1. Introduction to Data
Data professionals analyze data to help businesses make better decisions. To do this, they use powerful techniques like data storytelling, statistics, and machine learning. In this learning step, you’ll begin your learning journey by exploring the role of data professionals in the workplace. You’ll also learn about the project workflow PACE (Plan, Analyze, Construct, Execute) and how it can help you organize data projects.
In this step you need to understand the:
The functions of data analytics and data science within an organization.
Identify tools used by data professionals.
Explore the value of data-based roles in organizations
Investigate career opportunities for a data professional
Understand the data project workflow
Action Points:
Foundations of Data Science | Coursera & Google | Financial aid available | 1 Week | Beginner
2. Mastering SQL
In today’s data-driven world, the ability to analyze and manipulate large amounts of data has become a critical skill for data scientists. SQL (Structured Query Language) is a powerful tool that allows data professionals to extract valuable insights from vast amounts of data.
However, mastering SQL can be a challenging task, especially for those new to the field. This learning step aims to bridge the gap between novice and advanced SQL skills for data professionals.
Action Points:
SQL Basics | SQL Bolt | Free | 1 Week | Beginner
Intermediate & Advanced SQL | Mode SQL | Free | 2 Weeks | Intermediate
Database for Data Science | Stanford University | Free | 2 Weeks | Intermediate
SQL Case Studies | 8-week SQL challenge by Danny Ma | Free | 2 Weeks | Intermediate
SQL Practice | DataLemur | Free | 1 Week | Intermediate
3. Mastering 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
Action Points:
Microsoft Excel Tutorial for Beginners | freeCodeCamp | Free | 1 Week | Beginner
4. Python Basics
Python programming language is a powerful tool for data analysis. In this learning step, you’ll learn the basic concepts of Python programming and how data professionals use Python on the job. You’ll explore concepts such as object-oriented programming, variables, data types, functions, conditional statements, loops, and data structures.
By the end of this step, you will:
Define what a programming language is and why data professionals use Python.
Create Python scripts to display data and perform operations
Control the flow of programs using conditions and functions
Utilize different types of loops when performing repeated operations
Identify data types such as integers, floats, strings, and booleans
Manipulate data structures such as lists, tuples, dictionaries, and sets
Import and use Python libraries such as NumPy and pandas
Action Points:
Get Started with Python | Coursera & Google | Financial aid available | 1 Week | Beginner
5. Python for Data Analytics
In this learning step, you’ll learn how to find the story within data and compellingly tell that story using Python. You’ll discover how data professionals use storytelling to better understand their data and communicate key insights to teammates and stakeholders. You’ll also practice exploratory data analysis and learn how to create effective data visualizations.
By the end of this step, you should be able to:
Apply the exploratory data analysis (EDA) process
Explore the benefits of structuring and cleaning data
Investigate raw data using Python
Action Points:
Exploratory Data Analysis in Python | edureka | Free | 1 day | Begineer
Python for Data Analytics| Coursera & Google | Financial aid available | 1 Week | Beginner
6. Statistics Basics for Data Analytics
As a data analyst, you need to know how to use statistics to analyze data and gain important insights. In this learning step, you will explore key concepts such as descriptive and inferential statistics, probability, sampling, confidence intervals, and hypothesis testing. You’ll also learn how to use Python for statistical analysis and practice communicating your findings like a data professional.
Here are some of the basic statistical skills you should master:
Describe the use of statistics in data science
Use descriptive statistics to summarize and explore data
Calculate probability using basic rules
Model data with probability distributions
Describe the applications of different sampling methods
Calculate sampling distributions
Construct and interpret confidence intervals
Conduct hypothesis tests
Action Points:
The Power of Statistics | Coursera & Google | Financial aid available | 1 Week | Beginner
Regression Analysis | Coursera & Google | Financial aid available | 1 Week | Intermediate
7. Mastering A / B Testing
Designing and the analysis of A/B tests, also known as split tests, are online experiments used to test potential improvements to a website or mobile application. Two versions of the website are shown to different users — usually the existing website and a potential change. Then, the results are analyzed to determine whether the change is an improvement worth launching. In this learning step, we will cover how to choose and characterize metrics to evaluate your experiments, how to design an experiment with enough statistical power, how to analyze the results and draw valid conclusions, and how to ensure that the participants of your experiments are adequately protected.
You need to learn the following topics about A/B testing:
Overview of A/B Testing
Policy and Ethics for Experiments
Choosing and Characterizing Metrics
Designing an Experiment
Analyzing Results
Action Points:
A/B Testing | Udacity | Free | 2 Weeks | Intermediate
Ultimate AB Testing Course with Python Coding | Udemy | Paid | 2 Weeks | Intermediate
9. Introduction to Machine Learning
Machine learning uses algorithms and statistics to teach computer systems to discover patterns in data. Data professionals use machine learning to help analyze large amounts of data, solve complex problems, and make accurate predictions. In this learning step, you will focus on the two main types of machine learning: supervised and unsupervised. You’ll learn how to apply different machine learning models to business problems and become familiar with specific models such as Naive Bayes, decision trees, random forests, and more.
Here are some of the basic machine-learning skills you need to know:
Apply feature engineering techniques using Python.
Construct a Naive Bayes model.
Describe how unsupervised learning differs from supervised learning.
Code a K-means algorithm in Python.
Evaluate and optimize the results of the K-means model.
Explore decision tree models, how they work, and their advantages over other types of supervised machine learning.
Characterize bagging in machine learning, specifically for random forest models.
Distinguish boosting in machine learning, specifically for XGBoost models.
Explain tuning model parameters and how they affect performance and evaluation metrics.
Action Points:
The Nuts and Bolts of Machine Learning | Coursera & Google | Financial aid available | 2 Weeks | Beginner
10. Build Your Portfolio
Building a data analytics portfolio is an excellent way to showcase your skills, experience, and expertise in the field of data analytics to potential employers or clients. A well-structured and diverse portfolio can demonstrate your ability to analyze data, extract valuable insights, and make data-driven decisions.
Here are some steps to help you create a compelling data analytics portfolio:
Define Your Goals: Determine the purpose of your portfolio. Are you targeting specific industries or job roles? Understanding your goals will help you tailor your portfolio accordingly.
Showcase Your Projects: Include a variety of data analytics projects that demonstrate different skills and techniques. For example:
2.1. Data Cleaning and Preprocessing: Show how you handle messy or incomplete data to prepare it for analysis.
2.2. Exploratory Data Analysis (EDA): Highlight your ability to visualize and interpret data to gain insights.
2.3. Predictive Modeling: Demonstrate how you build machine learning models to make predictions.
2.4. Data Visualization: Showcase compelling and informative data visualizations.
2.5 Real-world Applications: If possible, include projects that have had a real impact on a business or organization.Provide Context: For each project, provide a brief description of the problem you were trying to solve, the data used, the techniques applied, and the results obtained. Explain the business or practical implications of your findings.
Use Different Tools and Technologies: Showcase your versatility by using a variety of data analytics tools and programming languages such as Python, R, SQL, Tableau, Power BI, or Excel. Highlight your proficiency with each tool.
Include Code and Documentation: Share your code, preferably on platforms like GitHub, to demonstrate your programming skills and your ability to write clean and well-documented code.
Visual Appeal: Design your portfolio in a visually appealing manner. Use clear, concise, and error-free language. Include charts, graphs, and screenshots to make it easier for viewers to understand your work.
Data Privacy and Ethics: Make sure you adhere to data privacy and ethics guidelines. If necessary, anonymize data or use publicly available datasets.
Continuously Update: As you complete new data analytics projects or gain new skills, keep your portfolio up to date to reflect your latest accomplishments.
Get Feedback: Share your portfolio with peers, mentors, or data analytics communities to get constructive feedback and suggestions for improvement.
Networking: Utilize social media platforms like LinkedIn to share your portfolio and connect with professionals in the data analytics field.
Looking to start a career in data science and AI and do not know how. I offer data science mentoring sessions and long-term career mentoring:
Mentoring sessions: https://lnkd.in/dXeg3KPW
Long-term mentoring: https://lnkd.in/dtdUYBrM
Thanks for sharing