Data Science Specialization
With our most popular program- Data Science Specialization- you will become a Data Science expert by covering various concepts like Python, Statistics, Machine Learning, and R Programming.
You can register for one of our free events to interact with our faculty or also attend a trial class.
Industry immersion sessions enable students to interact with Big Data Analytics practitioners from top companies like Twitter, Facebook, Vodafone, Barclays, PwC, Zomato, etc. to make them understand how their organizations use Big Data & Data Science.
Click here to view one such session taken by Sandeep Tompala- Senior Manager- Data Consulting, Vodafone.
- Next Batch Start Date: 6th January
- Class Timing: WE:Sat,Sun(08 am - 10 am), WD:Tue,Thurs ( 9 pm - 11 pm ), Fast track batch = Weekend (WE) + Weekday (WD).
- Mode: Online
- Certification of Completion: Yes
- Certification: Yes, Subject to clearing exam
EITHER OF THE TWO CORE MODULES MENTIONED BELOW 0/3
- 1. Data Analytics with Python and R
- 2. Machine Learning
- 3. Deep Learning with NLP
Data Analytics with Python and R(5 WEEKS) 0/9
- 1. Module 1: Data Science Introduction & Use Cases
- 2. Module 2: Python Basics: Basic Syntax, Data Structures
- 3. Module 3: Python Basics: Loops, If-elif statements, Functions, Exception Handling
- 4. Module 4: Statistics 1: Measures of central tendency, Population, Sample, Probability Distribution
- 5. Module 5: Statistics 1: Normal and Binomial Distribution, Random Variable, Pictorial Representations
- 6. Module 6: Python Advanced: Numpy, Pandas
- 7. Module 7: Python Advanced: Data Manipulation, Matplotlib
- 8. Module 8: Exploratory Data Analysis: Data Cleaning, Data Wrangling
- 9. Module 9: Exploratory Data Analysis: Data Visualisation
Machine Learning (5 WEEKS) 0/10
- 1. Module 1: ML Introduction & Use Cases
- 2. Module 2: Statistics 2 – Inferential Statistics
- 3. Module 3: Linear Regression
- 4. Module 4: Logistic Regression
- 5. Module 5: Decision Trees, Random Forest
- 6. Module 6: Modelling Techniques(PCA, Feature Engineering)
- 7. Module 7: KNN, Naive Bayes
- 8. Module 8: Support Vector Machines(SVM)
- 9. Module 9: Clustering, K-means
- 10. Module 10: Time Series Modelling
Deep Learning with NLP(5 WEEKS) 0/9
- 1. Module 1: Introduction to NLP & Deep Learning
- 2. Module 2: Word Embeddings
- 3. Module 3: Word window classication
- 4. Module 4: Introduction to Artifcial Neural Networks
- 5. Module 5: Introduction to Tensorflow
- 6. Module 6: Recurrent Neural Networks for Language modelling
- 7. Module 7: Gated Recurrent Units(GRUs), LSTMs
- 8. Module 8: Recursive Neural network
- 9. Module 9: Convolutional Neural Networks for sentence classification
All classes take place over the weekends in the mornings. There’ll be one class of 2 to 2.5 hours on Saturdays and Sundays each. This means that you can now acquire in-demand skills without compromising on your schedule.