Data Science Specialization

This course is designed to cover all aspects of Data science with core concepts.

This class is a advanced comprehensive class on Data science with Python programming language and Machine Learning. This course focuses on Python for Data Science, Statistics, Linear Algebra, Probability, Machine Learning and Tableau with hands on training on solving real world problems using Data Science.

This 120-hours of Data Science course covers all the major machine learning methods and Python modules for implementing them.

Main Features

  • 120 hours of intensive training
  • Regular Assignments
  • Get free access to learning materials and books
  • In house GPU powered servers for training complex datasets.

Industry Projects

Learn by practicing real-life industry based projects on Data Science sponsored by top companies across industries

  • Engage in collaborative projects with student-mentor interaction
  • Learn and solve problems in-guidance with expert mentors
  • Get regular feedbacks on the projects and learn to improve your skills.

Project 1

Walmart Recruiting – Store Sales Forecasting

One challenge of modeling retail data is the need to make decisions based on limited history. If Christmas comes but once a year, so does the chance to see how strategic decisions impacted the bottom line.

In this recruiting dataset, job-seekers are provided with historical sales data for 45 Walmart stores located in different regions. Each store contains many departments, and participants must project the sales for each department in each store. To add to the challenge, selected holiday markdown events are included in the dataset. These markdowns are known to affect sales, but it is challenging to predict which departments are affected and the extent of the impact.

 

Project 2

IMDb Movie Analysis

The IMDB Movies Dataset contains information about 14,762 movies. Information about these movies was downloaded with the purpose of creating a movie recommendation app. The data was preprocessed and cleaned to be ready for machine learning applications.

 

Project 3

Retail-Giant Time Series Sales Forecasting

“Global Mart” is an online store super giant having worldwide operations. The store caters to 7 different market segments and in 3 major categories. They want to forecast the sales and the demand for the next 6 months, that would help you manage the revenue and inventory accordingly.

 

Project 4

Uber Supply-Demand Gap

You may have some experience of travelling to and from the airport. Have you ever used Uber or any other cab service for this travel? Did you at any time face the problem of cancellation by the driver or non-availability of cars?

Well, if these are the problems faced by customers, these very issues also impact the business of Uber. If drivers cancel the request of riders or if cars are unavailable, Uber loses out on its revenue.

The aim of analysis is to identify the root cause of the problem (i.e. cancellation and non-availability of cars to and from the airport) and recommend ways to improve the situation. As a result of the analysis, we should be able to present to the client the root cause(s) and possible hypotheses of the problem(s) and recommend ways to improve them.

Our Student’s Reviews

Introduction to Data Science

1
History behind Data Science
2
Introduction to Data Mining
3
Roles of a Data Scientist
4
Selection Bias
5
Introduction to various Data Formats
6
Implications and Applications of Data Science
7
Data Science models, tools and packages

Introduction to Python

1
Why Python for Data Science
2
Installing Python Anaconda distribution
3
Jupyter Notebooks Walkthrough
4
Python Data Science Packages Overview
5
Basic Programming Concepts

Python Programming

1
Python Variables
2
Arithmetic and Comparison Operators
3
Decision Making
4
Loops
5
Data Types in Python
6
Functions and Classes
7
Python Objects
8
Exception Handling

Numpy package

1
Numpy overview
2
Importing Numpy
3
Numpy Arrays
4
Numpy Basic operations
5
Global functions
6
Selecting and Retrieving Data
7
Data Slicing
8
Iterating Numpy Arrays
9
Indexing: Arrays of Indices, Boolean Arrays
10
Views and Copies
11
Shape Manipulation
12
Stacking and Splitting

Pandas package

1
Pandas overview
2
Importing Pandas
3
Series and DataFrames
4
Series Operations and Manipulations
5
DataFrame Object

Python Advanced for Data Operations

1
Slicing, Merging and Joining operations in DataFrame
2
Changing the index of a DataFrame
3
DataFrame Concatenation
4
Reading various datas
5
Data Munging
6
Grouping and Aggregation
7
Reshaping Data
8
Analysing data for missing values
9
Missing values imputation: Fill with constant, Forward filling, Mean
10
Removing Duplicates
11
Redundant data drop operations
12
Transforming Data

Data Visualization with MatPlotLib

1
Introduction to Data Visualization
2
Importing MatPlotLib
3
Plotting Basic Graphs: Line, Bar, Histogram, Pie Chart
4
Graphs Manipulations and Styling
5
Multiple Plots
6
Plotting graphs by reading DataFrames
7
Setting axes, limits and ticks
8
Saving a Plot

Seaborn Package

1
Introduction to Seaborn
2
Importing Seaborn
3
Introduction to BoxPlot
4
Axes Plots using Seaborn
5
Various Statistical Plots using Seaborn

Introduction to Statistics

1
Basic Terminologies in Statistics
2
Branches of Statistics
3
Descriptive Statistics
4
Inferential Statistics
5
Variables
6
Quantitative vs Qualitative

Sampling

1
Sampling Data with and without replacement
2
Sampling Methods, Random vs Non-Random
3
Measurement on Samples
4
Random Sampling methods
5
Various other Sampling methods
6
Biased vs Unbiased Sampling
7
Sampling Error

Exploratory Data Analysis

1
Measures of Central Tendencies
2
Measures of Dispersion
3
Percentiles
4
Empirical Rule
5
Scoring
6
Outliers

Distributions

1
Introduction to Distributions
2
Normal Distribution
3
Central Limit Theorem
4
Normalization
5
Bernoulli and Poisson Distributions, etc
6
Normality Testing
7
Skewness
8
Kurtosis
9
Measure of Distance
10
Euclidean, Manhattan and Minkowski

Hypothesis and ANOVA

1
Hypothesis Testing
2
Null and Alternate/Research Hypothesis
3
P-value
4
Two tailed, Left tailed
5
Type I and Type II error
6
Parametric vs Non-Parametric Testing
7
T-Test : One sample, Two sample, Paired
8
Introduction to ANOVA
9
One way ANOVA
10
Two way ANOVA
11
Non Parametric Test : Chi-Square test and Wilcoxon Signed Rank, etc

Correlation

1
Types of Correlation
2
Weak and Strong Correlation
3
Correlation Analysis

Regression

1
Introduction to Regression
2
Type of Regression
3
Cost Function or Loss function
4
Simple Linear Regression
5
Multiple Linear Regression
6
Categorical Data
7
Logistic Regression

Introduction to Machine Learning

1
Introduction to Machine Learning
2
Applications of Machine Learning
3
Different types of Machine learning
4
Supervised vs Unsupervised
5
Batch vs Online
6
Modelling ML
7
Machine Learning Algorithms
8
Regression, Classification, Association and Clustering
9
Multiclass Classification

Regularization

1
Overfitting and Underfitting
2
Train Test Split
3
K fold cross validation

Model Evaluation and Selection

1
Regression Evaluation
2
Confusion Matrix
3
Precision and Recall
4
ROC Curve
5
F1 score

Mathematics for Machine Learning

1
Linear Algebra
2
Calculus
3
Probability

Supervised Learning

1
Univariate Linear Regression
2
Multivariate Linear Regression
3
Linear Discriminant Analysis
4
Logistic Regression
5
K-Nearest Neighbors (KNN)
6
Learning Vector Quantization
7
Support Vector Machine
8
Naive Bayes Classifier
9
Decision Trees
10
Random Forest
11
Adaboost
12
Markov

Unsupervised Learning

1
K-Means Clustering
2
Apriori Association

Dimensionality Reduction

1
Introduction to Dimentionality Reduction
2
Key Concepts of Dimensionality Reduction
3
Principal Component Analysis (PCA)

Natural Language Processing Basics

1
Introduction to Text Analytics
2
NLTK in Python
3
Tokenization
4
Text Cleaning
5
Stemming and Lemmatization
6
Sentiment Analysis

Deep Learning

1
Introduction to Perceptrons
2
Artificial Neural Network (ANN)
3
Tensorflow
4
Multilayer Perceptron
5
RNN
6
CNN
7
Applications of Deep Learning

Data Visualization with Tableau

1
Tableau Interface
2
Dimensions and measures
3
Filter shelf
4
Distributing and publishing
5
Connecting to Data Sources
6
Extracting and Interpreting data
7
Plots with Super Store data
8
Time Series Data
9
Forecasting
Classes will be Both Classroom & Live Instructor-led online training. you can choose as per your comfort. weekdays as well as weekend classes are available.
Yes, you can attend free demo class anytime.
No problem, You can attend backup class for the same.

Please contact us for more details.


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Enrolled: 780 students
Duration: 180
Lectures: 171
Level: Advanced

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Working hours

Monday 9:30 am - 6.00 pm
Tuesday 9:30 am - 6.00 pm
Wednesday 9:30 am - 6.00 pm
Thursday 9:30 am - 6.00 pm
Friday 9:30 am - 5.00 pm
Saturday Closed
Sunday Closed