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How To Become a Data Scientist Without any Technical Background


By LekhanaPublished 3 months ago 6 min read

Data science and AI are highly lucrative career choices today which is why I decided to pursue my career in this exciting field. This article is based on my observations as a data science newbie who is still learning with a double major in accounting and economics. Everything I'll be saying here is strictly based on my personal experience and some research I did on my own.

Tips to get started in data science without a technical background

1. Begin with Excel

The main purpose of Excel, a very well-liked business program, is to gather and store data. As a result, Excel has the majority of the data that you can use. Understanding how to manage and analyze data in Excel is a more accessible starting point for someone with a non-technical background and probably no programming knowledge. It will serve as a critical foundation for your career as a data scientist.

Learning fundamental Excel skills like cell referencing, creating simple formulas, and utilizing aggregation tools like SUM, AVERAGE, MAX, and MIN, as well as logical operations like IF, AND, and OR, is a good place to start. After mastering the fundamentals, you can move on to understanding how to use lookup functions like INDEX/MATCH, VLOOKUP, NESTED IFS, and conditional aggregation functions like COUNTIF(S), SUMIF(S), and AVERAGEIF(S) to extract information from data.

The following stage will be to learn how to use pivot tables, which will make it easier and more structured for you to aggregate and summarize data. You can learn how to make visuals and dashboards utilizing Pivot Charts, Slicers, Filters, and other tools in addition to Pivot Tables. Learning these concepts with an online data science course will offer you a fundamental grasp of how to interpret unprocessed data and visualize your conclusions.

2. Make better use of data using Power BI

The next step you can take is to replicate principles learned in Power BI once you've gotten the hang of dealing with data in Excel. Power BI includes more sophisticated features for working with data, even though it functions similarly to Excel in terms of cleaning, analyzing, and visualizing data.

You can concentrate as a beginning on learning how to utilize Power Query to connect to data, clean it, and carry out simple transformations. Power BI's data modeling function teaches you to model relationships between data tables. Additionally, you can learn how to use DAX to model your data and conduct calculations. The creation of visualizations and dashboards in Power BI utilizing both pre-built and custom visualizations, as well as tools like filters, slicers, bookmarks, page navigation, etc., will after that be beneficial to examine. To share and collaborate on Power BI reports, it would also be beneficial to understand how the Power BI Service operates.

3. Learn the basics of Python

Although Excel and Power BI are excellent tools for data analysis and visualization, data scientists frequently employ a variety of packages in the Python programming language to carry out more complex descriptive, prescriptive, and predictive data analysis, including machine learning.

You must first understand some python language fundamentals, such as general grammar, dealing with variables and expressions, control flow (loops and conditional statements), functions, data types, and data structures, before using the language for these data analysis functions. With a thorough grasp of these ideas, you should be able to use Python to build straightforward logic-based applications and lay the groundwork for learning the language for data science.

4. Learn the fundamentals of Python for Data Science.

You can begin learning Python for Data Science, where you'll discover several python libraries you can use to work with data once you have a firm grasp of the fundamental Python ideas. Pandas, NumPy, Matplotlib, and Seaborn are significant ones. The last two are for visuals, while the first two are for mathematical and statistical calculations.

Exploratory Data Analysis, a Python for Data Science feature, allows you to use these packages to extract, clean, analyze, and visualize data. If statistics is not your strong suit, you might wish to take some introductory statistics training since EDA strongly relies on statistics to provide meaning to data. SciPy is another Python toolkit that is useful for statistical and mathematical analysis. By enrolling in data science courses in India, you can master Python tools and libraries which offer special assistance to non-technical people.

5. Discover Basic Machine Learning

Exploratory Data Analysis is more of a descriptive analysis, so if you feel confident using it, you can move on to understanding machine learning, which is the prediction side of data science. You could start by researching well-liked supervised machine learning methods for regression and classification (linear regression) (Logistic Regression). You must be familiar with the scikit-learn Python library to use these techniques without starting from scratch with any new code.

After acquiring a general understanding of these two, you can learn more categorization algorithms like Decision Trees, Random Forest, Naive Bayes, and Support Vector Machines. Decision Tree Regression and LASSO Regression are two other regression algorithms.

6. Study SQL

You should learn SQL to work with relational databases once you're more accustomed to using Excel and other common data sources. As a data scientist, you will frequently interact with vast amounts of SQL data because businesses frequently use it to store structured data. PostgreSQL and MySQL are the two SQL databases that are most often used. As a beginner, you can start with one of them.

7. Enter the Advanced Zones

All the areas I mentioned above were meant to give you a basic grasp of the many components of a data science portfolio and some hands-on experience with them. There is still a lot to learn after this, and you can explore any of these more complex fields:

Use NoSQL to work with unstructured data.

Other advanced machine-learning ideas include:

Deep learning and neural networks.

Reinforcement learning.

Natural language processing.

Unsupervised learning (clustering, association).

Utilizing tools like Hadoop, Apache Spark, etc., to work with big data.

8. Advanced analytics using Tableau and Power BI.

Even though these advanced topics may sound intimidating, I think the best way to approach them is to pick certain areas and specialize in them based on your passion. Then, after specialization, you may move on to other areas if you want to be a more well-rounded data scientist.

Version control, online collaboration, and cloud computing are further factors.

Collaboration and information sharing are important aspects of a data scientist's job. When working alone or with a team, version control technologies like Git and GitHub let you keep track of changes to your code.

Additionally, communities where you may connect with other data scientists, learn from them, and share expertise, are provided by online collaboration platforms and forums like Kaggle and Towards Data Science. You may practice with a variety of datasets and data science codes on Kaggle, in particular.

The ability to utilize online processing, storage, and other resources is provided through cloud computing platforms like Microsoft Azure, Amazon Web Service, and Google Collab, among others.

Hope you this article was informative enough and encouraged you to get started your data science career. So Begin your first step by learning the basics. Acquire the IBM certification by joining the best data science courses in India, and get hired by top tech companies like Amazon, Google, etc.


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