In today’s digital world, every business and organization collects and uses data to build better products, target the right customers, improve efficiency, and even forecast future demand.
They say that data is the new oil – and now is the perfect time to enter the data analytics job market.
According to PayScale, the average salary for entry-level roles in analytics is around $55,492 per year. The average salary for a skilled analyst is about $88,928 per year. Even if you are a beginner to programming in Python, you can learn the essential skills for data analysis quickly if you are consistent.
In this article, I’ll go over what Data Analytics skills you'll need to know, and how to prepare for and ace interviews to land a Data Analyst position with Python.
What Does a Data Analyst Do?
As a data analyst, your primary responsibility is transforming raw data into meaningful insights.
Usually, the job description involves cleaning and organizing data to make sure that the quality of data is good. You'll also perform statistical analysis, interpret trends in complex datasets, build models, and create visualizations to communicate findings effectively. This information will help teams make business decisions and get valuable insights for the company's managers and key stakeholders.
Market research analysts collect and evaluate consumer and competitor data. A business analyst for Walmart could analyze purchase trends and identify seasonal patterns during events like Black Friday, Christmas, and New Year. This data could help the company expect higher demand and re-stock.
A data analyst at IKEA might analyze customer preferences in different rural and urban regions to better strategize which products to sell.
Data plays a role in every stage of a company, from market sizing and customer acquisition to advertising, customer journey, final conversion rate, and data-driven decisions.
Since I started working in data science, I have always felt like a little detective uncovering patterns and hidden knowledge. Are you now excited to learn how to become a data analyst? Let’s start with actionable insights.
Essential Technical Skills to Develop
The first step while preparing for any role is identifying and learning the right skills. Here are the essential and in-demand skills you should learn to become a data analyst:
Python Programming
One of the most crucial skills for a data analyst is proficiency in the Python programming language. Python is widely used in organizations to perform various tasks such as handling datasets, cleaning and manipulating them, and carrying out statistical analysis.
The popularity of Python stems from its ability to support a plethora of open-source packages and libraries and its flexibility and user-friendliness. I am confident that Python will continue to be an indispensable tool for data analysts in 2024.
If you’re new to Python, you can check out the Introduction to Python course on Hyperskill with hands-on projects, where I contribute as an expert. You don't need any degree to start learning.
But Python is vast – where should you start?
Start by learning basic syntax and data structures like lists, dictionaries, classes, and so on.
Once you are comfortable with the basics, get familiar with the essential libraries like Pandas (to read and manipulate data frames), Numpy (for statistical analysis), Matplotlib, and Seaborn for data visualization (creating plots).
- Here's a helpful course that teaches you Pandas and Python for Data Analysis.
- This course teaches you how to use Matplotlib for data visualization.
- Here's an in-depth guide to using NumPy for scientific computing in Python.
SQL
SQL (Structured Query Language) helps you interact with large relational databases. You should learn how to create and update SQL tables, perform filtering and aggregation, and extract insights. MySQL is a commonly used syntax.
You can check out the SQL course for beginners on Hyperskill. And if you want a text-based overview, here's a full handbook that teaches you all the SQL basics you'll need to know.
Data Visualization Tools and Software
Analysing the data is the process, but presenting your insights is the final destination. You must master visualization analytics tools like Tableau software or Power BI to create dashboards and reports.
As a data analyst, you may have to present your findings to non-technical teams interpretably. There are also many advanced methods, like interactive dashboards and geographic mapping, for visualizing spatial data to help make informed decisions.
Statistics
Probability and Statistics cover a wide range of essential concepts for anyone working with data. You should know the basic types of distributions, such as Normal, poisson, and skewed, and how to handle each.
Many metrics, like mean, median, and standard deviation, can help analyze numerical variables and identify anomalies or outliers. P-value and Hypothesis testing are also critical.
Here's a tutorial on the top Stats concepts to know before getting into Data Science if you want to check your skills.
Excel
Even though most of us are familiar with Excel basics, you should learn functions like VLOOKUP, HLOOKUP, INDEX, MATCH, and IF statements for data manipulation.
Understanding how to use PivotTables for summarizing and analyzing large datasets and enabling dynamic data exploration is crucial.
If you want to learn more about how you can use Excel for data analysis, here's a course on that.
Develop Your Portfolio
The data analytics industry is highly profitable but also fiercely competitive. More than simply working through courses and acquiring skills is required to stand out.
To become a successful data analyst, you must build a portfolio of projects demonstrating your abilities.
Once you're familiar with the relevant technology, identify a problem that requires analysis and locate a publicly available dataset. Analyze the dataset using various methods and extract any meaningful insights. If you don't have a degree, focus on making your portfolio the best you can.
Kaggle is a best friend to any data analyst beginner. Numerous datasets are available in all fields, from movie reviews and tweets to medical X-rays. Open notebooks allow you to see what expert data scientists have worked on with the same dataset. This is a great way to get guidance on approach and inspiration for ideas to try out.
For example, take the popular Kaggle dataset of IMDB Movie reviews. What can you do with it? I’ll share a few ideas to help you get started.
You can begin at a basic level by calculating statistics to summarize critical metrics such as average rating, distribution of ratings, and the most reviewed genres.
Then you could use natural language processing (NLP) techniques to perform sentiment analysis on the movie reviews.
Next, create visualizations to present findings effectively. For instance, plot sentiment scores over time, visualize the distribution of reviews across genres, or create a word cloud highlighting frequently used words in positive and negative reviews.
Tailoring your projects to align with your interests and the specific requirements of potential employers will make your portfolio stand out in a sea of applicants.
For example, if you want to work in healthcare, do a project that adds value to the field. Remember, it's not just about the code; it's about telling a compelling story with the data.
Finally, you'll want to scrape and analyze real-time data. Build a tool that tracks social media sentiment about a brand or analyzes website traffic patterns.
How to Build a Good CV
The first stage of any job application is shortlisting based on your CV (or résumé). Creating a concise and technically sound CV/résumé to increase your odds is crucial.
Your CV must be based on your educational background, coursework information, achievements, prior internships or work experience, and extracurriculars.
Let me share a few tips on creating a compelling CV or résumé:
- Custom CVs: When creating your résumé, customize it to the job you are applying for. Emphasize the skills and projects that are most relevant to the specific role. If appropriate, you can also include any extracurricular activities demonstrating your ability to manage a team. But you must provide only accurate information – this should go without saying, but embellishing your résumé beyond your actual experience is unacceptable.
- Quantify your achievements: Instead of mentioning that you conducted data analysis, mention specific projects, tools used, and your impact. For example, you could say that you increased website conversion rate by 15% through A/B testing. Remember to add any Python libraries, frameworks, and tools you used.
- Keep it concise and visually appealing: Recruiters review hundreds of résumés and may need help reading each line in the first round. So make a résumé that simultaneously conveys your skills and experience highlights. Use bullet points, clear headings, and formatting when needed to highlight certain aspects.
Tips to Ace the Technical Interview
The final stage is the technical interview. Below, I have gathered some tips that will help you understand what your preparation might involve, along with examples of questions you might encounter. Remember that each case is unique and you should use these as general guidelines.
First, make sure you practice coding a lot. You can use platforms like HackerRank or LeetCode. Remember that transparent and efficient code is vital for passing an interview. For example, you might be asked to describe the correct syntax for the reshape() function in NumPy.
Next, make sure you are comfortable working with SQL. You'll need to know how to handle complex queries, joins, subqueries, and data manipulation in SQL. A question like "How do you subset or filter data in SQL?" or "What is a Subquery in SQL?" could come up.
You should also be prepared to discuss and demonstrate your skills in data visualization. You should be able to explain your choices in visualization for different types of data. For instance, "How is joining different from blending in Tableau?" or "What is the difference between Treemaps and Heatmaps in Tableau?"
You'll also want to have a good understanding of statistics. Be prepared to discuss statistical concepts like mean, median, mode, standard deviation, correlation, and regression analysis.
You might be asked to interpret data or explain the significance of statistical findings in a business context, such as "Explain the term Normal Distribution” or “How do you treat outliers in a dataset?”
Next, make sure you have a solid foundation in data cleaning and preprocessing. Be ready to talk about experiences with cleaning and preparing data, involving dealing with missing values, outlier detection, and normalization.
Knowing tools like Pandas in Python can be particularly beneficial. An example question could be, "How can you add a column to a Pandas Data Frame?"
Be comfortable with data-driven decision making. You might be asked to explain how you have used data to inform decision-making in past experiences in order to demonstrate your ability to draw conclusions from collected data and use it for the company's business decisions.
You should also be able to showcase your past work. If possible, bring examples of your past work or projects, such as a portfolio or detailed case studies.
Be ready to discuss the challenges faced, how you approached them, and the outcomes. Questions like "Have you ever run an analysis on the wrong set of data? How did you figure out your error?" can be expected.
Also, don't neglect behavioral skills. Be prepared for behavioral questions that explore your problem-solving skills, teamwork, and ability to handle deadlines and pressure. Reflect on your past experiences and be ready to share stories that highlight these skills.
And finally, brush up on your industry knowledge. If the company operates in a specific industry (like finance, healthcare, retail, and so on), having some background knowledge or experience in that industry can be advantageous. Tailor your preparation to understand the unique data challenges and opportunities in that sector.
Remember, each company may have a different focus in their technical interviews, so try to get as much information as possible about the interview format beforehand. This way, you can tailor your preparation to meet their specific expectations.
Conclusion
Becoming a data analyst is a marathon, not a sprint.
If you are interested in a career as a data analyst, Python is an excellent language to learn. It is a versatile tool that allows you to manipulate, analyze, and visualize data effectively. By mastering in-demand skills such as Python, SQL, Data Visualization tools, Statistics, and Excel, you can set yourself up for success in the data analytics job market.
Also, building a portfolio of projects showcasing your abilities is crucial to stand out as an entry-level data analyst. The data analytics industry is rapidly growing, and there is a high demand for qualified professionals.
So, start learning and experimenting with data today to land your dream job as a data analyst in Python.
Embrace the learning, celebrate the small wins, and don't be afraid to ask for help. Good luck with your goals and data analyst career path!
Thank you for reading! I'm Jess, and I'm an expert at Hyperskill. You can check out an Introduction to Python course on the platform.