Guide to Start Data Science Path

Its better late than never. Yes, that’s true if you are thinking of starting the Data Science Journey. Your first step is the most important and decisive one. Think wisely and then only act.

Data Science is not like traditional Application Programming where you are given the defined problem to solve and you have a standard design pattern to implement. Here your Analytical Skills and love for Mathematics and Statistics is tested every single day.

An affinity towards the Data Analysis and Interpretation is a much needed ask here. The Good the story teller you are, the Better Data Scientist you will become.

Once you have the basic Mathematics [i.e. Derivatives and Integrals] and Statistics [along with Probability Theory] concepts clear, start with the in-depth understanding of Machine Learning Models mentioned below:

1. Supervised Models
   • Regression Models
   • Classification Models
2. Unsupervised Models
   • Clustering
   • Association Rules
   • Recommendation Engines
3. Time Series Forecasting
   • MA – Moving Averages
   • ARMA
   • ARIMA/ARIMAX
   • ES – Exponential Smoothing

Once You master the Machine Learning, then only start the Deep Learning Models and Frameworks as mentioned here:

1. RNN, CNN, LSTM
2. NLP and Voice Processing [including Chatbots]
3. Image and Video Processing
4. DL Frameworks like Keras, Theano, CNTK, Torch, TensorFlow, etc

---

For further knowledge, one can participate in Kaggle Competitions and go through below links for good reads:

• Kaggle
• KDnuggets
• AnalyticsVidhya

---

---

Refer this GitHub Link for Python Data Science Step By Step Training via Jupyter Notebooks.

Now you can listen Data Science Podcast on the go @ Spotify

---

Watch Here for more Details —