data science for a non-technical background
1. Start with Excel
Excel is a widely known business software program that is mostly used for data collection and storage. As a result, Excel contains the majority of the data you can work with. Understanding how to manage and analyze data in Excel is a simpler way to get started for someone from a non-technical background who probably has no programming knowledge. This will prove to be a very helpful foundation in your path as a data scientist.
Learning the fundamentals of Excel, such as cell referencing, formula writing, and the use of logical functions like IF, AND, and OR, as well as aggregate operations like SUM, AVERAGE, MAX, and MIN, is a good place to start. After mastering the fundamentals, you can begin learning how to use conditional aggregation functions like COUNTIF(S), SUMIF(S), and AVERAGEIF(S), as well as lookup functions like INDEX/MATCH, VLOOKUP, NESTED IFS, and others to extract information from data. The next stage will be to learn how to use pivot tables, which will make it easier and more organized for you to aggregate and summarize data. You can learn how to use Pivot Charts, Slicers, Filters, and other tools to create visualizations and dashboards in addition to Pivot Tables. You will gain a fundamental understanding of how to interpret unprocessed data and visualize your findings by mastering these ideas.
2. Do more with Data with Power BI
The next stage is to repeat principles learned in Power BI when you have a basic understanding of working with data in Excel. Although Power BI provides more sophisticated options for working with data, it functions similarly to Excel in terms of cleaning, analyzing, and displaying data.
As a novice, you can concentrate on learning how to connect to data, clean it up, and carry out simple operations using Power Query. With Power BI’s data modeling function, you may learn how to model the relationships between data tables. Additionally, you can learn how to use DAX to model your data and conduct calculations. Following this, it will be helpful to examine how to leverage Power BI’s built-in and custom visualizations, together with tools like filters, slicers, bookmarks, page navigation, and more, to develop dashboards and visualizations. To facilitate sharing and teamwork on Power BI reports, it would also be beneficial to have a basic understanding of how the Power BI Service operates.
3. Learn Introductory Python
Although Power BI and Excel are great tools for analyzing and visualizing 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, 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.
4. Understand the basics of Python for Data Science
Following your comprehension of those fundamental ideas, you can begin learning Python for Data Science, which will teach you how to interact with data using a variety of Python libraries. Seaborn, Matplotlib, NumPy, and Pandas are important ones. While the latter are for visualizations, the first two are for statistical and mathematical computations.
These packages can be used to extract, clean, analyze, and visualize data as part of the exploratory data analysis section of Python for data science. If statistics is not your strong suit, you might wish to enroll in beginning statistics classes, as EDA primarily relies on them to interpret data. SciPy is another Python library that can be used for mathematical and statistical analysis.
5. Learn Introductory Machine Learning
You can learn machine learning to get to the predictive side of data science after being acquainted with exploratory data analysis, which is more of a descriptive analysis. Learning popular supervised machine learning techniques for classification (Logistic Regression) and regression (Linear Regression) can be your first step. To apply these methods without having to write any code from scratch, you will need to be familiar with the scikit-learn Python package.
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.
6. Learn SQL
You might want to consider learning SQL to work with relational databases after you feel more at ease with data in Excel and other popular data sources. SQL is frequently used by businesses to store structured data, and as a data scientist, you will frequently encounter vast volumes of data. I advise you to learn PostgreSQL and MySQL, the two most popular SQL databases. You can begin with one of them as a novice.
7. Move into the Advanced Areas
All of the topics I covered above were meant to give you a head start so you could gain some knowledge and familiarity with the many components of a data science portfolio. There is still a lot to learn after this, and you can explore any of these more complex fields:
using NoSQL to work with unstructured data.
Advanced Machine Learning concepts like Deep Learning and Neural networks, Reinforcement Learning, Natural Language Processing, Unsupervised learning (clustering, association),, etc.
Working with Big Data using tools like Hadoop, Apache Spark, etc.
Advanced analytics with Power BI/Tableau.
While these advanced areas might seem overwhelming, I believe the best way to go about it is to pick specific areas and specialize in them based on your interest, and after specializing, you can then move on to the other areas if you want to be a more all-around data scientist.
8. Other Considerations: Version Control, Online Collaboration, Cloud Computing.
One major aspect of a data scientist’s work is collaborating with others and sharing information. Version control tools like Git and GitHub help you to keep track of changes in your code, whether working alone or collaborating as a team.
Furthermore, forums and online collaboration tools like Kaggle and Towards Data Science offer a community where you may interact with, learn from, and exchange knowledge with other data scientists. You may experiment with a variety of datasets and data science codes, especially on Kaggle.
Lastly, cloud computing platforms such as Google Colab, Amazon Web Services, and Microsoft Azure give users access to internet computation, storage, and other resources for more sophisticated and complicated analytics.
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