Being a new and fast-growing field, Data Science is in desperate need of skilled individuals. With lucrative opportunities and pay scales, enterprises around the globe have been in search of skilful professionals to work for them.
You too can make use of this possibility and have a career of your dreams. But becoming a data scientist isn't an overnight thing. It takes time and effort. So, How do we start to learn data science at right foot? We will find out.
Following are the few steps you could follow to learn data science.
- Find If Its Right for You
Before fixing on to this career choice, you have to make sure you are totally interested in this. You can ask following questions yourself to find if its right for you.
• Do you really enjoy programming and statistics?
• Are you willing to work in a field where you have to learn about the new techniques and technologies constantly?
• Are you okay with job titles like Data Analyst, business analyst etc. ?
If you have yes for an answer, then you can start learning Data Science right away. - Mathematics
You have to get familiar with a few topics in Maths in order to conquer data science. The main topic you need to study are the following
• Probability - A lot of data science works are based attempting to measure the probability of events. Textbooks are a good source of information for this subject.
• Statistics - This branch of mathematics deals with interpreting and analyzing the data. Fortunately, great textbooks are available online for you to refer.
• Linear Algebra - This branch of maths covers the study of vector spaces and linear mapping among this space. Linear algebra is a must to understand how machine learning algorithms work.
Once you are familiar with programming and various libraries, you may not have to dive deep into these mathematical details. But to understand them properly, you will need a sound base in these mathematical topics. - The Programming
Data Science community has chosen Python and R as their primary languages for programming due to various advantages. You have to learn and practice programming in these two languages at least for the following topics.
• Data Analysis - NumPy and Pandas, are the two common libraries used for data analysis in Python. Tidyverse is a popular compilation of packages in R for data analysis.
• Data Visualization - Matplotlib is the most used data visualization tool in Python. The most popular plotting library in R is ggplot2.
• Machine Learning - Python mostly make use of SciKit-learn library to do the machine learning works. When it comes to R, it offers a huge variety of packages including CARET, PARTY, random forest and many other.
When you complete these steps, you have a solid base required for a Data Scientist. Even if you find it hard to learn all this stuff on your own, the courses on data science prodegree by Imarticus is available to help you master the Data Science. The course provides comprehensive coverage of statistics and data science along with hands-on training on the leading analytical tools. so, stop wasting your time start preparing for your data science career right away.
