data-science-online-training-in-hyderabad

Data Science Training

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1. Introduction to Data Science

What is Data Science?
What is Machine Learning?
Who are Data Scientists?
What skills are required to become a Data Scientist?
Why should a data scientist learn both R as well As Python?
Is there importance for Statistics in Data Science?
Where do we implement Data Science in real time?
In which sectors of business, do we implement Data Science?
What kind of Projects do they work on?
Each and Every technique will be explained with a Real-time Example.
What is the difference between Machine Learning, AI, and Deep learning?

2. Business Statistics

a. DATA TYPES

Continuous Variables
Ordinal Variables
Categorical Variables
Time Series
Miscellaneous

b. Descriptive Statistics

Continuous Variables
Ordinal Variables
Categorical Variables
Time Series
Miscellaneous

c.Data Distributions

Normal Distribution
Binomial Distribution
Weibull Distribution
Poison Distribution
Exponential Distribution

d. Central Tendencies

Mean, Median, mode
Standard Deviation
Probabilities
Variance
Plotting

Basic and Business Statistics

Distance formulas
Similarities
Minkowski Distance
L1 Norm
Lemon Norm
Manhattan Distance
Taxi cab
Supremum
Lmax Norm
Euclidean Distance
Cosine Similarity
Hamming Distance
Levenshtein Distance
Simple Matching Coefficients
Jaccard's Equation
Differences between Distances and Similarities
Product of Dot Vectors
Binary values
Mahalanobis Distance
Matrix Multiplication
Variance
Conditional Probabilities
Difference between Statistics and probability
Confusion Matrix
Error Types
Mean Squared Error
Root Mean Square Error
Accuracy Measures

3. Introduction to Machine learning and Artificial Intelligence

How to deal with a prediction problem?
How to apply different forecasting techniques?
Supervised Learning
Unsupervised learning
Optimization
Sentiment Analysis
Statistical modelling

4. Machine Learning – Statistical Modelling

Simple Linear Regression

Simple Linear Regression with Gradient Descent
Linear Regression with Quadratic equation
Stochastic Gradient Descent Mode
Error Minimization Model
Linear Regression for classification
Estimating the coefficients
Assessing the accuracy of the coefficient estimates
Multiple Linear regression
Finding a new dependent Variable as a prediction
How to find a Slope and Intercept

Logistic Regression

Spline Regression
Sigmoid, Rigid and Lasso Regression
The Logistic Model
Estimating the Regression Coefficients
Making predictions
Multiple Logistic Regression
Nonlinear Regression

Linear Discriminant Analysis

Exploratory Data Analysis
Single, Variant Model Analysis
Multi-Variant Model Analysis
Quadratic Discriminant Analysis

Naïve bayes

Support Vector Machines

Maximum Margin Classifier
What is a hyperplane?
What are marginal Hyper planes?
Classification using a separating Hyperplane
SVM Linear Model
SVM Non-Linear Model
SVM Kernel Method
SVM Power Kernel Method

K-Nearest Neighbors

Advantages of KNN
Disadvantages of KNN
How to find an appropriate K value?
How to we use KNN for classification?
How do we use KNN for regression?
How to find the Nearest Neighbor

Ada-Boost / Adaptive- Boosting

Random Forest

What are Random Forests?
Overview of Random Forests
Classification with Random Forests
Implementation of Random Forests

Decision trees

How to build a decision tree
What are classification and their uses?
What is a decision tree?
Creating a Decision tree
Information Entropy
Information Gain
Examples
Gini Index and Entropy methods

Extra Trees

Market basket Analysis

Characteristics of Market Basket Analysis
Applications of Market basket Analysis
Association rules

5. Forecasting Analysis

Why Forecasting?
Features common to all Forecasts
Elements of a good Forecast
Types of Forecast
Qualitative Methods
Quantitative Methods
Techniques for Averaging
Techniques for Trend
Techniques for Seasonality
Personalizing Trend
De-seasonalizing Trend
Predictive Analysis
Various approaches to solving a problem
Pros and Cons of Machine Learning Algorithms
Data Leakage in Machine Learning

Re- Sampling and Ensembling Techniques

Bagging
Boosting
RF techniques

Gradient Boosting Method

Trees for prediction
Trees for classification

K-means Clustering

Clustering
Clustering techniques
Hierarchical clustering

Time Series

Qualitative Methods
Quantitative Methods
Moving Averages
Smoothing Techniques
Exponential Smoothing
Weighted Moving Averages
ARMA
ARIMA
Trend and Seasonality forecasting
Linear
Quadratic
Exponential
Auto-Regressive

Semi-Supervised Learning / Reinforcement Learning

6. Miscellaneous

Taxonomy Creation
Sentiment analysis
Text classification models
Process of tree building
Problem of Underfitting
Problem of overfitting
Pruning a tree back

7. Python for Data Science

Introduction to Python
Basics of Python

Number Systems
Operators
Loops
Functions
List
Tuple
Set
Dictionaries

Why Python for Data Science
Data Munging with Python
Introduction to pandas, Numpy
Data loading with Pandas
Descriptive Statistics with Pandas
Sort, Merge, and Join with pandas
Getting to understand the structure of Matplotlib
Different plotting techniques
Correlation matrix plotting
Indexing and Slicing with Pandas
Grid Search
Pre-processing techniques
Model Validation
Deployment
Metrics

8.Artificial Neural Network

Feed Forward neural network
Elman Neural network
Jordan Neural network
Weight initialization
Learning Rate calculation
Self-Connected Neural Network
Recurrent Neural Network

9. Deep Learning

Convolution Neural Networks
Long Short-Term Memory
ReLU
ELU
PRELU
Image Processing in Clarifai

10. Practical Use Case of How to Implement Prediction Models on data

How to define a problem / prepare the problem

1a: Load the libraries
1b: Load the respective data set

How to Summarize the data

2a: Descriptive Statistics
2b: Data visualization

How to prepare the data

3a: Data Cleaning
3b: Feature Selection
3c: Data Transformations

How to Evaluate Algorithms

4a: Split-out validation datasets
4b: Test options and evaluation metrics
4c: Spot-check algorithms
4d: Compare Algorithms

How to Improve Accuracy/Results

5a: Algorithm Tuning
5b: Using Ensembling Techniques

How to Print Results

How to Finalize the Model

7: Predictions on Validation Dataset

11. Introduction To R:

Introduction of R
Basics in R- lists, matrices, arrays, data frames etc.
If statements and conditional loops
Merging
String manipulations
Functions
Pre-processing techniques
Cost functions
Re-Samplings
Implementations of Algorithms
MODEL VALIDATION AND DEPLOYMENT:
Error measurement
RMSE, MSE
Misclassification Rate
Confusion Matrix
AUC (Area Under the Curve)
ROC curve
How to create a Data Frame?
Grouping on the Class variable
Converting different formats
Working on all Machine Learning Techniques in R.
Machine Learning terminology for Model Building.
Which Algorithm performs Best
Highly efficient ML Algorithms
Bagging Decision Trees
Ansembling Techniques
Boosting Techniques
How to consider which algorithm is best?

12. Analytical visualization in Tableau

Features of Tableau
Connecting Tableau to a Data File
Navigating Tableau – Dimensions and Measure
Creating a Calculating Field
Adding Colors
Adding Labels
Formatting
Creating groups and Bins
Combining two measures in a single chart
Working with Hierarchies
How to do Sorting?
Dual Axis Chart
Doing Analytics in Tableau
How to forecast using tableau
How to Model a Trend Line
How to custom Distribution band
How to custom a reference line
How do summarize the Totals Constant line and Average lines?
Calculations in Tableau
Sort and Filters
Charts
Bar Graphs
Pie Charts
Line chart
Bubble Chart
Scatter Plot
Box plot
Tree Map
Histogram
Waterfall chart
Gnat chart
Cross Tab
Tableau Dashboard

Offerings as a part of course

2 real-time projects END to END explanation with pseudo-code
All classes with real-time examples
Datasets with codes
End to End project workflow explanation
Assignments and Assessments
Data sets for Practice purposes
Real-time interview questions
Mock interviews

Tools

Basic concepts regarding the open source software – “R” and “Python”
Simple illustrative examples to be tried to gain familiarity with the software and complete the process in “Excel”

DATA SCIENCE ONLINE TRAINING INISTITUTE IN HYDERABAD

What is Data Science?

Data Science is the area of study which involves extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes. It helps you to discover hidden patterns in the raw data. The term Data Science has emerged because of the evolution of mathematical statistics, data analysis, and big data. Data Science is an interdisciplinary field that allows you to extract knowledge from structured or unstructured data. Data science enables you to translate a business problem into a research project and then translate it back into a practical solution.

Cyber Eagle Educational Services Pvt. Ltd. Data Science with R certification training course makes you an expert in data analytics using the R programming language. This course enables you to take your Data Science certification into a variety of companies, helping them analyze data and make more informed business decisions.

The Data Science with R programming course covers data exploration, data visualization, predictive analytics, and descriptive analytics techniques with the R language. You will learn about R packages, how to import and export data in R, data structures in R, various statistical concepts, cluster analysis, and forecasting.

Eligibility

This Data Science training with R programming is beneficial for all aspiring data scientists including, IT professionals or software developers looking to make a career switch into analytics, professionals working in data and business analysis, graduates wishing to build a career in analytics, and experienced professionals willing to harness Data Science in their fields

Pre-requisites

There are no prerequisites for this Data Science with R certification course. If you are a beginner in Data Science, this is one of the best courses to start with.