Step 1: Start with math. You don’t need to be a math expert, but understanding a few key areas will set you up for success. Focus on linear algebra, calculus, and probability & statistics.

Step 2: Learn Python. It’s the go-to language for machine learning because of its simplicity, large community, and powerful libraries that make building models easier. Focus on these key libraries: NumPy, Pandas, Matplotlib/Seaborn, and scikit-learn.

Step 3: Get hands-on with basic machine learning models. Focus on mastering supervised learning, where models are trained on labeled data. Start with linear regression, logistic regression, decision trees, and random forests.

Step 4: Once you’ve built your first models, the next step is improving them. Learn hyperparameter tuning and cross-validation.

Step 5: Move into deep learning for more complex tasks like image recognition and natural language processing. Start with neural networks, backpropagation, and deep learning frameworks.

Step 6: Learn to deploy your models. Start by creating simple APIs using Flask, then move on to cloud platforms like AWS, Google Cloud, or Microsoft Azure to scale and host your models as remote services.

Step 7: Build a strong portfolio by showcasing personal projects, Kaggle competitions, and GitHub repositories.

Storytelling with Data: A Data Visualization Guide for Business Professionals

Author: Cole Nussbaummer Knaflic

Written by the CEO and founder of Storytelling With Data, SWD is a book that emphasizes the importance of data storytelling in data analysis and provides six useful steps on how to do it.

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

Author: Wes McKinney

This book teaches you the fundamentals of using Python for data manipulation, processing, cleaning, and crunching. Real-world case studies are covered, along with an introduction to data science tools and instructions on how to use Matplotlib to build useful visualizations. Other techniques include loading, cleaning, manipulating, combining, and reshaping data. 

SQL QuickStart Guide: The Simplified Beginner's Guide to Managing, Analyzing, and Manipulating Data With SQL

Author: Walter Shields

This book provides a thorough introduction to SQL with digital resources such as workbooks and reference guides, and an example database and SQL browser software. It addresses subjects like relational database communication, database structures, important SQL queries, and marketing SQL expertise to prospective employers. The book also offers suggestions on marketing newly acquired SQL abilities to possible employers.

Learning R: A Step-by-Step Function Guide to Data Analysis

Author: Richard Cotton

This book offers a step-by-step introduction to the R language, covering environments, looping constructions, packages, and data structures. It then covers the data analysis processes, including loading, cleaning, and converting data. The second section is a great resource for individuals unfamiliar with programming languages, as it offers further insight into exploratory analysis and modeling. 

Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data

Author: EMC Education Services

This book introduces key techniques and tools for Big Data analytics, guiding readers from basic methods to advanced methods like classification, regression analysis, clustering time series, and text analysis.

1. Eloquent Ruby

Author: Russ Olsen

This book starts by answering many simple tactical questions on Ruby and then goes on to address bigger questions related to building methods and classes. You will find several tips and tricks, such as when to use tiny methods and operator overloading, and when you should avoid them. It also emphasizes the “Ruby way” of thinking and problem solving to write “eloquent” and beautiful Ruby code.

2. The Well-Grounded Rubyist

Author: David A. Black

This book is divided into three major parts: Ruby foundations, Built-in Classes and Modules, and Ruby Dynamics. It begins with how to write your first Ruby program and goes on to cover more advanced topics such as threading, reflection, and callable objects. It also covers all the new Ruby features, such as lazy enumerators, keyword arguments, and Module#prepend.

Which other books would you add to this list?

Step 1: Start with a Bachelor’s degree in Business, IT, Economics, or Engineering. While not mandatory, formal education helps you understand business processes, data, and systems, which are core to a Business Analyst role.

Step 2: Develop the ability to work with data and extract insights. Focus on Excel and Advanced Excel, SQL for data querying, Basic Statistics and Data Interpretation, and Data Visualization tools like Power BI or Tableau

Step 3: Learn Stakeholder Analysis, Business Process Mapping, Requirement Gathering and Documentation, and a basic understanding of domains like Finance, Healthcare, or E-commerce

Step 4: Apply your knowledge in real-world scenarios. Work on tasks like creating reports and dashboards, documenting business requirements, analyzing workflows, and identifying improvements, and focus on solving real business problems. Start with internships, freelance projects, or entry-level roles.

Step 5: Showcase your skills through a portfolio by including case studies of business problems you solved, dashboards and data analysis projects, and documentation samples like BRDs or user stories. Use platforms like GitHub or portfolio websites to present your work professionally.

Step 6: Validate your expertise with recognized certifications like CBAP® Certification Training. Certifications help you stand out and build credibility in the job market.

Step 7: As you gain experience, choose a specialization: Data Analyst or Analytics-focused BA, Product or Agile Business Analyst, or a Domain Specialist, like Finance or Healthcare.

Career Path and Growth in Business Analysis:

• Entry-Level Roles: Junior Business Analyst or Associate Analyst
• Mid-Level Roles: Business Analyst or Product Analyst
• Senior Roles: Senior Business Analyst or Lead Analyst
• Advanced Roles: Business Analysis Manager or Consultant

Eloquent JavaScript: A Modern Introduction to Programming

Author: Marijn Haverbeke

A best-selling book that provides a deep dive into the JavaScript language. Every chapter has several projects to give you a hands-on experience of writing real-world applications. You will also learn how to script browsers, use the DOM effectively, harness Node.js to build servers, and make artificial life simulations.

You Don't Know JS Yet: Get Started

Author: Kyle Simpson

This is the go-to book for all the basics, including the building blocks and more niche things that you can do with JavaScript. Even if you are a more experienced programmer, this book can help you learn the more complex and trickier parts of the language.

Which other books would you add to this list?

1. Download SAS University Edition

The SAS University Edition is a good starting point. It’s free to download, easy to explore, and helps you build a solid foundation.

Remember that the free version has limited functionality, and SAS’s advanced features are behind a paywall. Pricing from vendors like SAS is typically confidential and client-specific, so that you can get a sense of the edition cost from SAS's site.

2. Brush Up Your SQL Know-how and Other Statistical Skills

SQL, regression modeling, time series analysis, ANOVA, and many other statistical skills are essential when you work with SAS. Get up to speed in these areas before you delve into the nitty-gritty of SAS itself.

3. Leverage Free Resources

There are tons of free resources available on the internet for looking to learn SAS.

For practice, visit developer hubs like GitHub. Benchmark your creations against sample code shared by SAS learners around the world. ‘The Little SAS Book’ is another useful resource for SAS learners.

4. Consider a SAS Certification

While freely available resources will help you get started with SAS, SAS certification courses will help you build a career in the field with live industry projects.

Inferential statistics, logistic regression, clustering and segmentation, decision trees, PROC IML, and PROC SQL are all powerful techniques that lie within the ambit of SAS, and you can gain a working knowledge of these tools by enrolling in a SAS certification training program.