Can one become a Data Scientist in 2 months?

Be it to become a Data Scientist or be it to become any particular role in any of the fields, 2 months is not enough. If you just want to know about Data Science and how it works, what is the importance of it in every sector and why Data Science is emerging, to have a detailed study on the above said would take you 6 to 8 months. 

Data Science is a broad subject and there are different roles that one can acquire after the completion of the Data Science Course. If you are a Data Science enthusiast then you should research this field know why Data Science is gaining importance and what changes would it make if you start to pursue a career in it!

Uprightly, 2 months is not at all enough on getting trained in this field. You need more than that and if you are driven to become a Data Science then it would take you 6 months to 8 months to get properly trained and you can start yourself for looking out for various job roles. So, one of the important thing is to find a right training institute for the same!

Now that there are many things that you should get skilled in if you want to become a Data Scientist. Like:

Programming Skills: Programming skills play an essential part in Data Science. So you should be skilled in Python or R as these two are considered the best programming languages in Data Science. Mainly, Python is grabbing a lot of attention and is popular in this field. Other than Python and R, other skills are equally important in Data Science, such as Excel, Tableau, Hadoop, SQL, and Apache Spark.

These days many of them usually prefer to get trained in Python and if you are also interested in getting trained in Python more than R, then these are the things under python that you should get trained in: Numpy, Pandas, Matplotlib, Seaborn, Scikit-learn, PyTorch.

Probability and Statistics: You should have a basic understanding of Probability and Statistics even before you undertake a Data Science Program. You should know to work on these. Data Science is used in different sectors to predict the behavior of the needs with the industry or the environment, so if we want to predict an outcome of a variable that can take one of many available values then we have to involve the mathematics of probability. So, you should be familiar with Mean, Median, Mode, Standard deviation and variance, Standard deviation, Variance, Correlation coefficient, and the covariance matrix, Probability distributions (Binomial, Poisson, Normal), Baye’s Theorem, A/B Testing. Other than Probability, Multivariable Calculus, Linear Algebra, Optimization Methods: so you should be familiar with: Cost function, Likelihood function, the Error function.

Even if you are a non-programmer, you can start the training from the scratch!

Data Wrangling: You should know to wrangle and clean that will help you derive critical insights from your data.

Data Preprocessing: You should know Data Preprocessing as it deals in such things as- Dealing with missing data, Handling categorical data, Encoding class labels for classification problems.

Machine Learning: Machine learning is an important section in Data Science. These are the important algorithms that you should get trained in Machine Learning like Supervised Learning and Unsupervised Learning.

• Supervised Learning: Supervised learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output.
• Unsupervised Learning: Unsupervised learning is where you only have input data (X) and no corresponding output variables. The goal of unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data.

Real-time Projects: Other than the above-mentioned skills you should work hands-on on any Capstone projects and Real-time Projects that would give you more exposure towards the theoretical training. By this, you can understand how to work on different cases and on Real-time cases. By working on projects it will be an add-on to your resume.

Be a Learner: This field of Data Science is evolving and there will be changes in technology happening every now and then, so you should be a learner always so that you be updated in this field.

Communication Skills: All the above-mentioned skills are an essential part of Data Science, but you would be the one working on different data, so you are responsible to communicate everything that can be understood by the other employees working in different domains. So for this, your communication skills should be very strong. 

These are all the skills that are needed for you to become a Data Scientist and if you work on these skills dedicated and being focussed you can grab a job easily. 

Data Science is an evolving field that would only be gaining its importance, so be updated accordingly..

Learnbay, a Bangalore based institute is one of the best places to study Artificial Intelligence, as the Artificial Intelligence Program provided here covers all the essential concepts of the subject, it helps aspirants to effectively understand and practice the concepts with various real-time projects.