Basic knowledge of Excel, Python, and SQL can help you start a data science career even with a non-tech background.

Do you want to build a data science career, but have no technical background? Well, it must not stop you from chasing your dream career. It's never too late to start over in a new direction in academics and here is how you can take up the path of data science even with a non-technical background.

Beginning from Scratch

Even without any exposure to working with data, one can begin with understanding how data is being leveraged by organizations and its industrial applications. Then one can curate a curriculum to prep oneself with the required technical skills. For instance to learn about programming languages, and other key concepts, one can register for courses. Online platforms like Udacity, KDnuggets, Dataquest, and more, offer online courses in data science. One must also be acquainted with basic mathematical concepts like linear algebra, calculus, probability, and statistics. This is important because while data science tools and tech will continue to change rapidly, the underlying math will not. One can also enroll in certification programs for data science. Earning a certification can improve one's skills and boost the chances of being a better data scientist candidate. 

Learning Excel is a Must

Excel is a very popular business software as it is mostly used to collect and store data. As a result, most of the data that you can work with can be found in Excel. As someone from a non-technical background who most likely has no programming experience, being able to understand how to manipulate and analyze data in Excel is an easier route to start and will prove a very useful foundation in your journey as a data scientist. You can start by learning basic Excel concepts like cell referencing, writing basic formulas, and using aggregate functions like SUM, AVERAGE, MAX, MIN, and logical functions like IF, AND, OR. Once you understand the basics you can then start learning how to retrieve information from data using conditional aggregate functions like COUNTIF(S), SUMIF(S), AVERAGEIF(S), and Lookup functions like INDEX/MATCH, VLOOKUP, NESTED IFS, etc. After this, the next step will be to learn how to work with Pivot Tables, which will help you summarize and aggregate data in an easier and more structured way. In addition to Pivot Tables, you can learn how to create visuals and dashboards using Pivot Charts, Slicers, Filters, etc. Learning these concepts will give you a basic understanding of how to get meaning from raw data and present your findings through visualization.

The Next Step Will Be Power BI

After getting the gist of working with data in Excel, the next step you can take is to replicate concepts learned in Power BI. While Power BI works similar to Excel with respect to cleaning, analyzing, and visualizing data, it has more advanced features for working with data. As a beginner, you can focus on how to use Power Query to connect to data, clean, and perform basic transformations. You can learn how to model relationships between tables of data using Power BI's data modeling feature. You can also learn how to create calculations and model your data using DAX. After this, it will be useful to look at how to create visualizations and dashboards in Power BI using both in-built and custom visualizations as well as features like filters, slicers, bookmarks, page navigation, etc. Another helpful tip would be to have a basic understanding of how the Power BI Service works to help share and collaborate on Power BI reports.

Familiarizing with Python

Although Power BI and Excel are great tools for analyzing and visualization of data, the python programming language contains a wide array of packages that are widely used by data scientists to perform more advanced descriptive, prescriptive, and predictive data analysis, including machine learning. To start using python for these data analysis functions, you first need to understand some basics of the python language such as the general syntax, working with variables and expressions, control flow (loops and conditional statements), functions, and data types, and data structures. A good understanding of these concepts should help you create simple logic-based programs with python and give you a solid foundation to learn python for data science.

Introduction to Machine Learning

Once you're familiar with Exploratory Data Analysis, which is more of a descriptive analysis, you can move on to the predictive aspect of data science by learning machine learning. You can start by learning common supervised machine learning algorithms for Regression (Linear Regression) and Classification (Logistic Regression). You will need to know the python package scikit-learn to help you implement these algorithms without coding from scratch. After getting the gist of these two algorithms, you can then learn more classification algorithms like Decision Trees, Random Forest, Naive Bayes, and Support Vector Machines. Other regression algorithms include Decision Tree Regression and LASSO Regression.

Learning SQL software

When you're more comfortable with working with Data in Excel and other common data sources, you might want to look into learning SQL for working with relational databases. SQL is commonly used by companies to store structured data and can contain large amounts of data which you will come into contact with a lot of times as a data scientist. The most common SQL software which I recommend you learn is PostgreSQL and MySQL. As a beginner, you can start with one of these.

Go for Real-Life Projects 

Gaining practice training and experience is the linchpin of securing a data science job in the top reputed companies. For this, one must focus on building a portfolio of projects that focus on solving real-world bottlenecks and inefficiencies. Obviously, there will be many candidates eying for the same data scientist job position. So, going for more focused project learning is a sure way to stand out in a crowd than the academic route. These projects also highlight one's ability to transfer theoretical skills into the creation of data models that have an impact on society and industry.

More Trending Stories: