What Should We Learn Before We Join a Data Science Course?

Getting into data science is exciting, but it can be tricky to know where to start. Did you see the demand for data scientists has recently grown by over 36%? 

Yet, many new learners dive into complex topics too quickly, missing out on essential basics that could make the journey smoother.

In this guide, we will discuss topic-by-topic what you need to know before joining a data science program — from math and programming fundamentals to the basics of machine learning. 

Understanding the Basics of Data Science

Data science is a field that combines statistical analysis, programming, and domain expertise to extract meaningful insights from data. It has become essential across industries, helping organizations make data-driven decisions. 

Here’s a foundational overview:

1. Defining Data Science:
   • Data science involves gathering, processing, analyzing, and interpreting large volumes of data to uncover patterns, correlations, and trends.
   • It aims to answer specific questions and support decision-making by transforming raw data into actionable insights.

2. Key Components of Data Science

• Data Collection: Gathering data from multiple sources, such as databases, web scraping, or sensors, to create a reliable dataset.

• Data Cleaning and Preprocessing: Preparing data by handling missing values, removing duplicates, and normalizing data, ensuring it is accurate and ready for analysis.

• Exploratory Data Analysis (EDA): Using statistical methods and visualization tools to understand data characteristics, spot patterns, and identify potential anomalies.

• Data Modeling: Applying machine learning algorithms to model data, aiming to predict outcomes or classify data points based on historical information.

• Data Interpretation: Drawing conclusions from analysis, interpreting model outputs, and presenting findings in an understandable way for stakeholders.

3. Core Techniques in Data Science:

• Statistics and Probability: Fundamental for understanding data distributions, hypothesis testing, and making inferences.

• Machine Learning: Employing algorithms like regression, clustering, and classification to make predictions or identify patterns.

• Data Visualization: Using tools like matplotlib, Tableau, or Power BI to create visual representations of data, making complex insights more accessible.

• Big Data Tools: Utilizing tools such as Hadoop and Spark to handle and process large datasets efficiently.

4. Common Tools and Languages:

• Python and R: Popular languages for data analysis and machine learning due to their rich libraries and frameworks.

• SQL: Essential for querying databases and managing structured data.

• Excel: Widely used for data manipulation and simple analysis, especially in business settings.

5. Applications of Data Science:

• Business Intelligence: Identifying sales trends, customer preferences, and optimizing pricing strategies.

• Healthcare: Predicting disease outbreaks, improving diagnosis accuracy, and personalizing patient care.

• Finance: Detecting fraudulent transactions, credit scoring, and managing investment risks.

• E-commerce: Personalizing product recommendations and analyzing customer buying patterns.

6. Data Science Workflow:
   • The workflow typically follows a sequence: Define the problem → Collect data → Clean and preprocess data → Perform EDA → Build models → Validate models → Deploy models → Monitor and iterate.

Understanding these basics provides a foundation for diving deeper into specialized areas, such as machine learning, natural language processing, or data engineering. 

For those interested in entering the field, mastering the fundamentals of data science is the first step toward a rewarding career in this rapidly growing industry.

Want to dive deeper?
Check out our blog, which will help you better understand what is data science.

Foundational Knowledge Areas

3.1 Mathematics and Statistics

Mathematics is the foundation of data science, with statistics at its core. Key concepts like probability and hypothesis testing are essential for Data Scientists to understand and interpret complex data effectively.

Before starting advanced courses, refreshing your high school algebra and basic statistics can be very helpful. A strong foundation makes learning data science easier and more effective.

Our Statistics for Data Science Free Course covers basics in linear algebra, calculus, and statistics. It’s a great way for beginners to build the skills needed to tackle more advanced topics and make data science less intimidating.

3.2 Programming Skills

Programming is one of the most essential skills for a data scientist. Python and R are the most popular languages in the field due to their versatility and the availability of libraries for data manipulation, visualization, and machine learning.

Python is the leading programming language among data scientists, with a large majority using it regularly as their preferred tool.

For those of you who are new to programming, we have our Free Python for Data Science course. It provides basics, syntax, and data structures, as well as useful libraries such as Pandas and NumPy that you may need to start working with the data.

You can check out our free courses to start your learning at no cost.

• Python for Data Science Free Course.
• R for Data Science Free Course.

3.3 Data Manipulation and Analysis

Data manipulation is a crucial step in preparing raw data for analysis. You’ll need to know how to clean, filter, and restructure data using libraries like Pandas (Python) or (R). 

Since data scientists spend a significant portion of their time on data wrangling, mastering this skill early will make the learning process much smoother.

Try out our Basics of Python Data Wrangling Free Course, where you’ll work with real datasets and gain hands-on experience cleaning and preparing data for analysis.

You can check out our free courses to start your learning at no cost.

• Step by step guide to become a data scientist Free Course.
• Introduction to Data Science Free Course.
• Data Science Foundations Free Course.
• Data Preprocessing Free Course.
• SQL for Data Science Free Course.
• Machine Learning with Python Free Course.

Familiarity With Data Visualization Tools

Visualization tools like Tableau, Power BI or even just basic libraries in Python (Matplotlib, Seaborn) are Key to convey a message about your findings.

Visual design is not only about prettifying graphs, but also making non-trivial data interpretable.

Presenting data visually is highly effective, as people process visual information much faster than text.

Explore our Free Data Visualization for Beginners course, where you can practice building impactful visuals that convey stories from data. These skills will help you stand out and make your findings easily digestible for any audience.

You can check out our free courses to get Familiarity with Data Visualization Tools at no cost.

• Pivot Tables in Excel Free Course
• Excel VBA for Beginners Free Course
• Tableau Dashboards For Beginners Free Course.
• Data Visualization with Power BI Free Course.
• Data Visualization using Tableau Free Course.

Understanding Machine Learning Concepts

Machine learning plays a significant role in data science (this is why). Having a strong understanding of the basics is very important. 

Many jobs in data science expect you to know how to create, assess, and improve models. Key topics to pay attention to are supervised vs. unsupervised learning, model evaluation, and how to avoid overfitting and underfitting.

If you’re ready to dive in, our Introductory Machine Learning Free course has a free module that covers important algorithms like linear regression and decision trees.

You will get hands-on experience with smaller datasets (this), which makes it easier to build your confidence; however, you can then progress to larger projects. Although it might seem challenging at first, it’s a great way to learn.

Soft Skills and Critical Thinking

Although technical skills are essential, soft skills (like critical thinking, problem-solving, and communication) are just as important. Most employers value communication skills as much as technical skills in data science roles.

Learning to interpret findings and communicate insights effectively is crucial. This will help you collaborate with team members and enhance your success in any data-driven role. However, developing these skills takes time and effort, so be patient with yourself.

Exploring Online Resources and Communities

Participating in professional communities like Kaggle, GitHub and Stack Overflow allows you to accelerate your learning process with a very tangible focus on use-cases. Your platform for growthLet’s communicate Among your peers to excel skills and face challenges together.

The connections you make here are key to growing and thriving in your data journey. 

Forums and competitions are an invaluable resource that will allow you to learn more about the kinds of challenges, but also exposure can be obtained by constantly participating in forum discussions.

Our data science courses offer a strong foundation for beginners, allowing you to connect with like-minded individuals and receive feedback from industry experts. 

By collaborating on projects, you’ll accelerate your learning and expand your professional network. With dedicated career support, you’ll get assistance with resume reviews, interview prep, and job search strategies.

Setting Your Own Goals

Setting clear goals is one of the most important things you can do to make learning data science easier. 

This might be something like learning the basics of programming, finishing a small project on your own or perhaps even participating in some sort of competition. These are the milestones that will motivate you through.

Think of data science as a man you’re, not a sprint; pacing yourself helps avoid burnout and improves retention. Our Goal-Setting Guide offers easy steps to help you define your goals and track progress along the way.

Ready to start your career? 

Enroll in the best data science courses provided by Great Learning.

Advance Your Career With A Data Science Certification

1. UT Austin Data Science and Business Analytics Program

UT Austin’s Data Science Course furnishes an extensive grasp of data science, encompassing foundational subjects (such as statistics and machine learning) and delving into advanced realms (including deep learning and big data).

The University of Texas at Austin’s data science courses stand out for a number of reasons, including:

• Faculty: Courses are taught by top UT Austin faculty and industry experts.
• Curriculum: Courses are modeled after UT’s on-campus graduate curricula.
• Hands-on projects: Students work on real-world problems.
• Mentorship: Students have access to an industry expert mentor.
• Program support: Students have access to a dedicated program support person 24/7.
• Networking: Students can network with peers and like-minded professionals.
• Interdisciplinary: Students take courses from both the Department of Statistics and Data Sciences as well as the Department of Computer Science.
• Industry-driven: Courses teach in-demand data skills through real-world examples.
• Critical thinking: Students need to be able to think deeply and critically about experiments, observational studies, etc.

Whether you’re looking to start a new career or advance in your current role, this course equips you with the skills and knowledge you need to succeed in the dynamic world of data science. For in-detailed information, you can check What Makes UT Austin’s Data Science Courses Stand Out?

2. PG Program in Data Science and Business Analytics

There are plenty of courses available, but the PG Program in Data Science and Business Analytics from Great Learning is a top choice! 

This course is perfect for those looking to combine data science expertise with business analytics knowledge. You’ll gain an in-depth understanding of statistical methods, data analysis, and machine learning, and learn how to apply these skills to real-world business challenges.

Key Highlights of the Program:

• Structured Learning & Flexible Schedule: Complete 12 months of online sessions with flexible learning options and personalized weekend mentorship sessions.

• Hands-on Exposure to Tools & Real-World Case Studies: Work with 15+ languages and tools (Python, SQL, Tableau, etc.) and analyze case studies from companies like Netflix, Uber, and Spotify.

• Practical Application & Projects: Apply your learning through 11 hands-on projects, 1 capstone project, and 40+ industry case studies.

• Concept Reinforcement & Career Support: Strengthen your skills with 40+ quizzes and receive dedicated career support, including resume reviews and interview preparation.

• Expert Faculty: Learn from world-renowned faculty and industry experts.

• Dual Certification: Earn certificates from UT Austin and Great Lakes to enhance your credentials.

This program suits well for those who are looking to start a career in data science and business analytics.

3. PG Program in Data Science and Engineering

The PG Program in Data Science and Engineering offers a comprehensive and structured learning experience designed to equip you with the skills needed to thrive in the data science field.

Some key highlights of program:

• Structured Learning: Participate in 9 months of online sessions, complete with placement assistance to help you transition into the workforce.

• Expert Faculty: Learn from award-winning professors affiliated with a top-10 business school in India.

• Supportive Environment: Benefit from dedicated learning and career support, including doubt-clearing sessions.

• Practical Application: Analyze over 13 real-world case studies to apply theoretical concepts in practical scenarios.

• Career Preparation: Receive assistance with resume reviews, interview preparation sessions, and four or more mock interviews to boost your confidence.

• Postgraduate Certification: Earn a postgraduate certificate from Great Lakes, a recognized name in education.

Ready to start your data science career? 
Check out the best online courses for aspiring data scientists and find the perfect path to kickstart your journey!

Conclusion

Getting into data science is smoother when you start with some basics in math, programming, and data skills. Using free resources, like our introductory courses, or joining a program like UT Austin Data Science and Business Analytics Program really helps.

Wherever you are in your journey, data science has hundreds of ways for you to learn and grow. So why not kick off your learning now, connect with others, and start turning data into insights? Let’s get going!

Frequently Asked Questions (FAQ’s)