How to start a career in data science
Welcome! If you want to explore the fascinating field of data science but are unsure of where to begin
Figure out what you need to learn
The area of data science may be very intimidating. You’ll hear a lot of people say that you can’t become a data scientist until you have mastered things like statistics, linear algebra, calculus, programming, databases, distributed computing, machine learning, visualization, experimental design, clustering, deep learning, natural language processing, and more. Simply said, that is untrue.
What precisely is data science, then? It involves posing thought-provoking questions and then using data to provide answers. The data science pipeline often looks like this:
Pose a query
- A mass information that might assist you in resolving the query.
- purge the data
- Investigate, evaluate, and display the data
- Create a machine learning model and assess it.
- Transmit results
It is not necessary to be an expert in deep learning, complex mathematics, or many other abilities mentioned above in order to use this methodology. However, it does need familiarity with a programming language and the capacity to manipulate data in that language. Additionally, although mathematical fluency is necessary to excel in data science, only a fundamental knowledge of mathematics is required to start.
Indeed, you could someday use the other specific abilities mentioned above to aid you with data science challenges. However, to start a career in data science, you do not necessarily need to be an expert in all of these areas. I’m here to assist you, and you may start right now!
Get comfortable with Python.
Both Python and R make excellent alternatives for data science programming languages. Although Python is more widely used in business and R is more used in academics, both languages provide various tools that help the data science workflow. I’ve instructed students in both languages in data science, but I usually favor Python. (This is why.)
To get started, you don’t have to learn both Python and R. You should instead concentrate on mastering a single language and its ecosystem of data science software. However, if you choose to use Python, which is what I advise, you might want to think about downloading the Anaconda distribution since it makes managing and installing packages on Windows, OS X, and Linux easier.
Additionally, you don’t have to master Python to go to step 2 of the process. Instead, concentrate on becoming an expert in the following: imports, functions, conditional statements, comparisons, loops, and comprehensions. You can put off anything else till later!
Check out my Python Quick Reference if you’re unsure if you know “enough” Python. Step 2 can be reached if you are familiar with the majority of that information.
Learn data analysis, manipulation, and visualization with pandas
You should become familiar with the pandas’ library if you want to work with data in Python.
Similar to an Excel spreadsheet or a SQL table, pandas offers a high-performance data structure (referred to as a “DataFrame”) that is appropriate for tabular data with columns of varied kinds. In addition, it has capabilities for handling missing data, filtering data, cleaning up untidy data, combining datasets, displaying data, and much more. Simply said, mastering pandas will significantly improve your data-processing productivity.
However, Pandas offers (perhaps) too many methods to complete the same work and has an excessive amount of functionality. Due to these qualities, learning about pandas and finding best practices might be difficult.
Use Scikit-Learn to learn about machine learning.
It would be best if you learned how to utilize the scikit-learn library for machine learning in Python.
The exciting aspect of data science is creating “machine learning models” to forecast the future or automatically extract insights from data. The most widely used machine learning library in Python is scikit-learn, and with good reason:
It offers a clear and uniform user interface for various devices.
Each model gives a wide range of adjusting settings while also picking acceptable defaults.
Its excellent documentation makes it easy to comprehend the models and learn how to apply them effectively.
Understand more about machine learning
The area of machine learning is complex. Scikit-learn gives you the tools you need to do machine learning effectively; however, it doesn’t directly address several crucial issues:
- Which machine learning model would “best” fit my dataset, and how can I know?
- How should I interpret my model’s findings?
- How can I determine if my model is likely to generalize to new data?
- How can I choose the characteristics that belong in my model?
- and so forth
Keep learning and practicing.
My best recommendation for honing your data science abilities is as follows: Find “the thing” that inspires you to put what you’ve learned into practice and to continue learning, and then do that. This may be doing individual data science projects, competing on Kaggle, taking online classes, reading books or blogs, going to meetings or conferences, or something else entirely! Your adventure in data science has just begun! Data science is a discipline with so much to learn that mastery would take more than a lifetime. Just keep in mind that getting started is all you need to jumpstart your data science career. If you’re seeking a career in data analytics or data science, head over to a data science course in Bangalore.