Guide to Start a Data Science Career with a Non-Tech Background
How to start a data science career with a non-tech background?
Do you long for a data science career but lack the technical know-how to get started? It shouldn’t deter you from pursuing your passion, however. With this guide, anybody, regardless of their prior experience or education, may begin a Career in Data Science.
What is Data Science?
Data science aims to employ cutting-edge analytics tools and scientific concepts to glean actionable insights from data for use in decision-making and strategic planning in business and other contexts.
Now, let’s look into how you can make a Career in Data Science:
Beginning from Scratch
You may get started with data even if you have no prior experience by learning how companies use data and the industries benefiting from it. Then you may tailor a learning plan to give you a leg up in critical technical areas, such as programming languages and fundamental ideas. In addition, you may find courses in data science on several online learning sites. A person’s chances of being hired as a data scientist go up if they have a certification attesting to their proficiency in the field.
Excel Training Is Required
Excel’s widespread adoption in the corporate world may be attributed to its use as a data-gathering and storage tool. Therefore, you can find most of the data you need in Excel. Knowing how to edit and analyze data with Excel is a more accessible way to begin your career as a data scientist, especially if you have no programming knowledge from an IT career for non IT Background and don’t have any programming knowledge. You may begin by familiarizing yourself with Excel’s fundamentals, such as cell referencing, formula writing, and using aggregate functions like SUM, AVERAGE, MAX, MIN, and logical operations like IF, AND, OR.
Once you have a firm grasp of the fundamentals, you may go on to study advanced techniques for extracting data, such as the use of conditional aggregation functions. After that, you may learn how to use Pivot Tables, which will make summarizing and aggregating data much simpler and more organized.
You may learn how to use Pivot Charts, Slicers, Filters, etc., to build visualizations and dashboards in addition to Pivot Tables. Mastering these ideas will give you the fundamentals necessary to extract insights from data and communicate those insights visually.
Power BI Is the Next Step
Once you’ve gotten the hang of manipulating data in Excel, you can go on to using Power BI’s techniques as you are from an IT career with non-IT Background. Data cleansing, analysis, and visualization in Power BI are comparable to Excel but offer more robust capabilities. If you’re just starting with Power Query, it’s best to concentrate on learning how to make connections, clean data, and apply simple transformations.
Power BI has a data modeling function where you may learn how to model the connections between different data tables. DAX may be used to model your data and do computations. The next step is to learn how to leverage Power BI’s in-built and custom visualizations and other tools like filters, slicers, bookmarks, page navigation, etc., to build visualizations. Sharing and collaboration are facilitated by having a fundamental familiarity with the Power BI Service.
A Primer on Python
When it comes to sophisticated descriptive, prescriptive, and predictive data analysis, including machine learning, the python programming language is commonly employed by data scientists. Even though both Power BI and Excel are excellent tools for analyzing and visualizing data, before attempting to use Python for these data analysis tasks, you should be familiar with the general syntax, working with variables and expressions, control flow (loops and conditional statements), functions, data types, and data structures. If you want to study Python for data science, having a firm grasp of these ideas will give you a head start.
Understanding Machine Learning
After studying descriptive Exploratory Data Analysis, you may learn predictive machine learning. Start by learning Regression (Linear Regression) and Classification methods (Logistic Regression). To apply these algorithms without coding from scratch, you’ll need a scikit-learn in python. After understanding these two algorithms, you can learn Decision Trees, Random Forest, Naive Bayes, and SVMs. Decision Tree and LASSO are other regression techniques.
Go for Real-Life Projects
The best way of gaining experience and securing a data science job is by trying your hands over real-life projects. For this, you must focus on building a portfolio of projects that focus on solving real-world inefficiencies. So, going for more focused project learning is the best way of standing out in a crowd among your competitors. These projects will let you highlight your ability to transfer theoretical skills into the creation of data models.
Conclusion
Gaining training and experience is key to landing a top data science job. Build a portfolio of initiatives to solve real-world bottlenecks and inefficiencies. Choosing project-based learning over academics can help you stand out with Data Science Certification. These initiatives show how to turn theoretical knowledge into data models that benefit society and industry. For more conceptual clarity into the concepts of Data Science, you can enroll for the courses offered by The IoT Academy.
