Data science is very much popular in today’s world scenario as there is a huge amount of data generated each day in different fields such as BFSI, Healthcare and Telecom. This training encompasses a conceptual understanding of Statistics, Machine Learning and Deep Learning using the Python and R programming languages.
Introduction to Data Science
• What is Data Science?
• Data science lifecycle
• Use Cases/applications/examples
• DS tools and technology
• Python 2.7 Vs 3.4
• Python programming fundamentals
• Data types and structures, variables, Control flows, and functions
• Python libraries
• Numpy, Pandas, SciKitLearn, MatPlotLib
• Introduction to R
• Data Frames
Data Extraction, Wrangling and Exploration
• Data Analysis Pipeline
• What is Data Extraction
• Types of Data
• Raw and Processed Data
• Data Wrangling
• Exploratory Data Analysis(EDA)
• Data Structures in Pandas - Series and Data Frames
• Basic Probability
• Conditional Probability
• Properties of Random Variables
• Entropy and cross-entropy
• Covariance and correlation
• Estimating probability of Random variable
• Understanding standard random processes
• Estimating parameters of a population using sample statistics
• Hypothesis testing and confidence intervals
• T-tests and ANOVA
• Correlation and regression
• Chi-squared test
• Compute and interpret values like: Mean, Median, Mode, Sample, Population and Standard Deviation.
• Compute simple probabilities.
• Explore data through the use of bar graphs, histograms and other common visualizations.
• Investigate distributions and understand a distributions properties.
• Manipulate distributions to make probabilistic predictions on data.
• Bar Graph, Histogram, Pi Chart, Line Chart, Box (Whisker) Plot, Scatter Plot, Heat map
Basic Machine Learning Algorithms
• Linear Regression
• Logistic Regression
• Decision Trees
• KNN (K- Nearest Neighbours)
• K-Means Clustering
• Naïve Bayes
• Dimensionality Reduction
• Random Forests
• Dimensionality Reduction Techniques
• Support Vector Machines
• Gradient boosting
Introduction to Deep Learning
• Tensor flow
• Neural Networks
• Biological Neural Networks
• Understand Artificial Neural Networks
• Building an Artificial Neural Network
• How ANN works
• Image recognition
• Image classification
Natural Language Processing(NLP)
• What is Time Series data?
• Time Series variables
• Different components of Time Series data
• Visualize the data to identify Time Series Components
• Implement ARIMA model for forecasting
• Exponential smoothing models
• Identifying different time series scenario based on which different Exponential Smoothing
model can be applied
• Implement respective ETS model for forecasting
8 Hrs per Weekend
Some FAQs :
What is the pre-requisite to learn Data Science
Since Machine Learning Algorithms use Statistics extensively atleast a
basic knowledge of Statistics will be helpful.
Prior experience with any programming knowledge though not mandatory will be an advantage
What are the various roles to pursue after the course
Data Analyst, Data aware Project Manager and Data Scientist are some of the roles that can be pursued post undertaking the course
What is the duration of the course.
Week-end batches are 4 hours per day for 10 weekends spanning a course of about 2 months
Weed-day batches are 6 hours per day Monday to Friday across 2-3 weeks.
What are the key skills of a Data Scientist
Statistics, Machine Learning(ML), Data Wrangling, Data Visualization & Communication, Software engineering, Data Intuition and Programming skills
What is the future of Data Science
According to Gartner, the self-learning (ML-powered) intelligent systems will continue to reign supreme in the technology marathon through the coming years which will result in creation of tremendous job opportunities.