3 Data Science & Data Analytics Skills to Learn

Two decades into the 21st century, we're not so unfamiliar with the concept of data. We know that data infiltrates every part of our lives. However, as Jay Baer, marketing and customer experience expert, puts it, "We are surrounded by data, but starved for insights."

And he's not wrong. A study by Finances Online estimates that the data consumption from 2020 to 2030 will reach 660 zettabytes—equivalent to 610 iPhones (128GB) per person. Imagine?

With the future of employment changing, now's the time to start building your repertoire of data knowledge.

What key skills are needed for data science and analytics?

Both data scientists and analysts should be well-versed in SQL and Python. In working with data streams and employing SQL and Python, several essential skills are crucial for effective data manipulation, visualization, and statistical analysis.

SQL (Structured Query Language)

Query Writing: Proficiency in writing SQL queries is fundamental. This includes SELECT statements, JOIN operations, GROUP BY clauses, and understanding various SQL functions to filter, aggregate, and manipulate data.

Data Cleaning and Transformation: Ability to clean and transform data within a relational database using SQL. This involves handling missing values, data normalization, and creating derived columns.

Window Functions: Understanding and using window functions for complex aggregations and analytics. This includes functions like ROW_NUMBER(), RANK(), and LAG/LEAD.

Indexing and Optimization: Knowledge of indexing strategies and query optimization to enhance the performance of SQL queries on large datasets.


Data Manipulation Libraries: Proficiency in using libraries like Pandas for data manipulation. This includes filtering, merging, grouping, and transforming data.

Visualization Libraries: Familiarity with data visualization libraries like Matplotlib and Seaborn to create meaningful plots and charts for data exploration and presentation.

Statistical Analysis: Understanding of statistical concepts and the ability to perform statistical analysis using libraries like NumPy and SciPy in Python. This includes hypothesis testing, regression analysis, and descriptive statistics.

Machine Learning Libraries: Basic knowledge of machine learning libraries such as Scikit-learn for implementing predictive models and clustering on streaming data.

Data Streaming Platforms

Understanding Streaming Architectures: Knowledge of the principles and architectures of streaming data platforms, such as Apache Kafka or Apache Flink.

Stream Processing: Familiarity with stream processing concepts and frameworks like Apache Kafka Streams or Apache Spark Streaming for real-time data processing.

Handling Time Windows: Ability to work with time-based windows in streaming data, which involves aggregating and processing data over specific time intervals.

Data Visualization

Dashboarding Tools: Proficiency in tools like Tableau or Power BI for creating interactive dashboards that provide real-time insights into streaming data.

Plotly and Bokeh: Knowledge of interactive visualization libraries like Plotly and Bokeh for creating dynamic and interactive plots in Python.

Custom Visualization Code: Ability to write custom code for creating visualizations, especially when dealing with unique or specialized data presentation requirements.

Communication Skills

Interpreting Results: The ability to interpret and communicate results effectively to both technical and non-technical stakeholders is crucial.

Documentation: Strong documentation skills to explain data processing workflows, data transformations, and analysis steps.

Working with data involves using SQL and Python to effectively manipulate, analyze, and visualize streaming data, providing valuable insights for decision-making in real-time scenarios. With these skills in hand, you'll be off to a great start in mastering data science or analytics.

CTA: Take the Tech Skills for insights on which tech skills to learn or potential career paths you should consider.

How do I teach myself data analytics?

Anyone with an interest in data analytics can learn using free online resources. Whether you're a beginner with a dash of curiosity for the world of data or a long-time professional looking to boost your knowledge, online courses are a great way to add necessary data skills to your arsenal.

Below are some of the best courses for beginners we could round up.

Google's Data Analytics Certificate

Google's Data Analytics Certificate covers the basics like R programming, SQL, Python, Tableau and more. Self-paced, this course gives you access to 150 employers after completion if you're interested in jumping into a data analytics career.

Many people have leveraged their Google certifications into part-time or full-time employment. With the right interview tips and industry connections, you could get your start in data analytics.

FreeCodeCamp's Data Analysis with Python

FreeCode is an online resource with over 9,000 tutorials covering everything from web development and data to quality assurance and cyber security. Bonus, they even provide interview prep for aspiring data analysts.

FreeCodeCamp describes the Data Analysis with Python course like this: In the Data Analysis with Python Certification, you'll learn the fundamentals of data analysis with Python. By the end of this certification, you'll know how to read data from sources like CSVs and SQL, and how to use libraries like Numpy, Pandas, Matplotlib, and Seaborn to process and visualize data.

Not too shabby for a free course.

Can I self study data science?

The same principle applies when it comes to learning data science concepts: free online courses! Check out some of the data science resources below.


Kaggle is chalked full of free tutorials and guides. You can build a solid data science foundation with Intro and advanced courses on SQL and Python. From there, you can go deeper with data science tutorials on machine learning, deep learning, and even artificial intelligence (AI) ethics and reinforcement learning.


DataCamp has long been a go-to for those looking to hone or learn new data science skills. Each introduction course is free to follow, and you can even choose a particular career path you're interested in and DataCamp will bring you through the necessary data science tutorials that'll look real good on your resume.

What is the most efficient way to learn data science?

There is no perfect way to learn data science or data analytics, as each person's learning style and how they retain information differs. But for most people, a combination of theoretical and practical experience alongside a supportive network will get them where they want to be.

Set clear learning goals

Self-learning takes a healthy dose of discipline. Try giving yourself goalposts to reach by a certain date, starting with the simplest concepts and working up. You can even reward yourself every time you grasp an idea (I know someone who buys Pokémon card packs), and keeping a good rhythm with your studies will keep the information fresh.

Find the right community

There are many online communities where you can connect with other data analysts and scientists. Reddit (r/datascience and r/dataanalysis), GitHub, and Stack Overflow are classics. Otherwise, you can check out Towards Data Science, Data Science Central, and Analytics Vidhya.