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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 Programming
• Installation
• Python
2.7 Vs 3.4
• Python
programming fundamentals
• Data
types and structures, variables, Control flows, and functions
• Python
libraries
• Numpy,
Pandas, SciKitLearn, MatPlotLib
R Programming
• Introduction
to R
• Vectors
• Matrices
• Factors
• Data
Frames
• Lists
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
Probability
• Basic
Probability
• Conditional
Probability
• Properties
of Random Variables
• Expectations
• Variance
• Entropy
and cross-entropy
• Covariance
and correlation
• Estimating
probability of Random variable
• Understanding
standard random processes
Inferential
Statistics
• Estimating
parameters of a population using sample statistics
• Hypothesis
testing and confidence intervals
• T-tests
and ANOVA
• Correlation
and regression
• Chi-squared
test
Descriptive Stats
• 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.
Data visualization
• 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
Advanced algorithms
• 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
Sentiment Analysis
Text Mining
Natural Language Processing(NLP)
Time Series
• 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
10 Weekends
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.