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- Want to break into data? Here's what you really need to know
Want to break into data? Here's what you really need to know
Karina Samsonova
September 20, 2024
If you’re making the leap into data science or data analytics, you might think learning Python or coding is the first step. But before you write a single line of code, let's talk about what really matters.
Spoiler alert: It's not about coding. It's about thinking.
Here's the truth: The most successful data professionals I know didn't start with Python. They started with something much more powerful - a data mindset.
Let's break down what you should focus on first:
1. Master the Fundamentals of Statistics
Before diving into any programming language, it’s crucial to understand the basics of statistics. Concepts like probability, distributions, and hypothesis testing are the backbone of data analysis. Without these, it’s like trying to build a house without a foundation.
For example, take Time Series Analysis. Looking at historical data, you notice sales always spike this time of year. Your statistical skills help you separate this seasonal trend from the campaign's impact.
Another example is Hypothesis Testing. You decide to run an A/B test. Group A sees the new campaign, Group B doesn't. After crunching the numbers, you find there's only a 5% chance the sales difference between groups is due to the campaign. In stats-speak, it's not statistically significant.
2. Learn to Ask the Right Questions
Data science isn't about having all the answers - it's about knowing which questions to ask. Sharpen your critical thinking skills. Practice framing business problems in data terms. This skill alone can set you apart in interviews.
Avoid the "Data for Data's Sake" Trap. I've seen so many analysts fall into this pit. They collect tons of data, create beautiful dashboards... and solve absolutely nothing. Why? Because they didn't start with the right questions. It is very important to understand what exactly the stakeholder is trying to achieve and why. By asking the right questions you will save the company’s money, time and save yourself from headache.
3. Get Comfortable with SQL
Most of the world’s data is stored in databases. Databases speak different languages, and one of them is SQL. Imagine you are travelling to Barcelona and you are thirsty. Saying “Una botella de agua, por favor” will help you to get a bottle of water. In SQL language it will be:
SELECT bottle_of_water
FROM STORE
WHERE water = 'cold' AND water_type = 'sparkling' SQL is simpler than Python and immediately applicable. Mastering SQL early gives you a practical advantage because you’ll be able to retrieve and manipulate data from day one. I personally learned SQL because I had to request data from a database administrator. It took ages to write down a list of all the columns I needed, specify the type (sum, average), and the level of detail (grouping by day, month). Sometimes I made mistakes and forgot to add grouping or a column. That back-and-forth was so exhausting that I learned SQL to do it myself.
4. Familiarise Yourself with Data Visualisation
They'll teach you how to paint pictures with data - a crucial skill for any data professional. Plus, they're user-friendly enough that you can start creating impressive visualisations in no time.
5. Understand the Data Lifecycle
Ever wondered how data gets from a user's click to your analysis? Understanding this journey is crucial. It'll inform every analysis you do and help you spot potential issues before they become problems. I like testing websites or apps while looking at the testing logs at the same time—it helps me “feel” the data and understand what happens in the database when I click this or that button. Being a customer of the product helps me understand the data lifecycle.
Why This Approach Works:
It builds a solid theoretical foundation
Develops essential non-coding skills (because data science isn't just about algorithms)
Provides immediate practical value (job-ready skills!)
Makes learning Python easier later on (you'll understand why you're coding, not just how)
Remember this: Programming is a tool, not the goal. Data science and analytics are about extracting insights, not writing the fanciest code.
So, start with these steps. Build your analytical thinking muscles. Then, when you do learn Python, you'll truly understand its power - and you'll be lightyears ahead of those who jumped straight into coding.
Your data journey begins with understanding, not coding. And trust me, this approach will make you a much stronger data professional in the long run.
Keep pushing 💪, future data rockstars!
Karina
Need more help?
Just starting with Python? Wondering if programming is for you?
Master key data analysis tasks like cleaning, filtering, pivot and grouping data using Pandas, and learn how to present your insights visually with Matplotlib with ‘Data Analysis with Python’ masterclass.
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