Data Science is one of the hottest areas in the 21st century. We can solve many complex problems using a huge amount of information. The way electricity has changed the world, information helps us to make our lives easier and comfortable. Every second, an enormous amount of data is being generated. The data may be in the form of text, image, speech or tabular.

As there is a lot of growth in the field of Data Science, in recent years, most of the companies have started building their own Data Science teams to get benefited from the information they have. This has created a lot of opportunities and demand for Data Science in different domains. For the next 5+ years, this demand would continue to increase. If we have the right skills, companies are ready to offer salaries more than the market standards. So, this is the right time to explore and gain skills which enables you to enter into this field.

We have discussed the importance and demand for data science in the market. Let’s discuss the high level skills that are required to become a Data Scientist:

- Statistics
- Linear Algebra
- Probability
- Calculus
- Machine Learning and
- Deep Learning!

I would suggest taking an in-depth course in Statistics, Linear Algebra and Probability because, they are not easy to master in a few days. NPTEL has recorded high-quality class-based courses that incorporate most of the topics that are required. Solid understanding of mathematical concepts will make you stand out of the crowd.

Now, let’s discuss different algorithms that are generally used in data science. We can broadly divide into Supervised, Unsupervised and Semi-supervised learning.

Now, let’s discuss different algorithms that are generally used in data science. We can broadly divide into Supervised, Unsupervised and Semi-supervised learning.

- Supervised learning:
- Classification:
- Bayes theorem
- K Nearest Neighbors (KNN)
- Logistic Regression
- Decision trees
- Random forest
- XGBoost
- Regression:
- Linear Regression
- Lasso Regression (L1 regularization)
- Ridge Regression (L2 regularization)
- Tree-based algorithms like XGBoost Regressor, etc.
- Unsupervised algorithms:
- Clustering:
- K means clustering
- Hierarchical clustering
- Density-based spatial clustering of applications with noise (DBSCAN)
- Dimensionality reduction:
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- Factor analysis
- Semi-supervised learning:
- Positive Unlabeled Learning (PUL)

Apart from the above-listed algorithms, some companies are also focusing on Deep Learning. It's better to have knowledge on:

- Neural Networks
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN) and
- Long Short Term Memory (LSTM)

The above list is based on my experience and you need to get updated with the latest technology, as there is a lot of development going on in this field.

In the rest of the post, I will list out the programming skills that are required. In “Data Science” world, we mainly hear about two programming languages, Python and R. Relatively, Python is becoming more popular than R. So, I would suggest learning Python if you are planning to enter into Data Science.

Some of the Python libraries that add value to your resume are:

- Numpy - Used to perform high-dimensional computations faster and it is memory efficient.
- Pandas - Built on Numpy and used for data manipulation and analysis.
- Matplotlib - is a Python 2D plotting library which produces quality figures similar to the plots in Matlab. (Reference: Matplotlib documentation)
- Seaborn - Built on Matplotlib and relatively easier to use than Matplotlib to plot high-quality visualizations.
- Plotly - Used to build interactive and beautiful visualizations. We can plot in both Python and R languages, and easy to integrate with Dash, a framework for building analytic web apps.
- Keras - Very easy to use Deep learning library for experimentation. This is a high-level API capable of running on top of TensorFlow, CNTK or Theano. (Reference: Keras documentation)

If you have good hands-on experience in Keras, you can start learning TensorFlow or you can directly start learning TensorFlow if you haven’t been into Deep Learning before.

Hope this blog post would help you start your career in Data Science. We cannot master it in one night. It is a time-consuming process. Be patient, believe in you and give yourself at least 6 months of time to explore this domain.

Thank you for reading my blog and supporting me. In the next post, I will post on “How to apply for a Data Science job?”. Stay tuned for my next post. Keep learning and sharing!!

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Very useful for the beginners. In this competitive world, one needs to have good knowledge of the above mentioned algorithms.

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