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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 Institute in Hyderabad

Data Science Online Training in Hyderabad

Data Science is one of the most in-demand fields in today's technology-driven world. It involves extracting meaningful insights from large volumes of structured and unstructured data using scientific methods, statistical techniques, algorithms, and programming. Businesses use Data Science to identify hidden patterns, understand trends, predict outcomes, and make data-driven decisions.

Data Science Online Training in Hyderabad at Cyber Eagle Educational Services Pvt. Ltd. is designed to help aspiring professionals develop practical data analytics skills using the R programming language. This training helps learners understand how to collect, process, analyze, and interpret data to solve real-world business problems.

Whether you are a graduate, IT professional, software developer, or working professional, our Data Science program helps you build the knowledge required to explore career opportunities in data analytics and Data Science.

Data Science with R Certification Training

Cyber Eagle Educational Services Pvt. Ltd. offers Data Science Online Training in Hyderabad with a focus on R programming and essential data analytics techniques. This course is designed to develop your understanding of statistical analysis, data visualization, predictive modeling, and data interpretation.

Through our Data Science Training, you will learn how to work with different types of datasets, apply statistical methods, and extract valuable business insights. The course introduces essential R programming concepts and analytical techniques that can be applied to real-world business scenarios.

Our training also helps learners build the skills required to pursue a Data Analyst Certificate and other relevant Data Science Certification opportunities.

What Will You Learn in Our Data Science Course?

Our Data Science Online Course covers essential concepts and techniques used in modern data analytics. The curriculum includes:

R Programming Fundamentals: Understand R syntax, variables, data types, functions, and programming concepts.
Data Exploration: Learn how to examine, clean, organize, and understand datasets.
Data Visualization: Create meaningful charts, graphs, and visual representations to communicate data insights.
Statistical Analysis: Understand descriptive statistics, probability, and statistical methods used in data analysis.
Predictive Analytics: Explore techniques for analyzing historical data and predicting potential outcomes.
Descriptive Analytics: Learn how to summarize and interpret data to identify trends and patterns.
R Packages: Work with useful R packages and libraries for data analysis and visualization.
Data Import and Export: Learn how to import and export datasets in different formats using R.
Data Structures in R: Understand vectors, matrices, lists, data frames, and other important data structures.
Cluster Analysis and Forecasting: Explore clustering techniques and forecasting methods for analytical applications.

These concepts provide a foundation for working with data and developing practical analytical skills.

Why Choose Data Science Online Training in Hyderabad at Cyber Eagle?

Choosing the right training program is an important step in building your career in analytics. Cyber Eagle Educational Services Pvt. Ltd. focuses on helping learners understand Data Science concepts through structured training.

Our Data Science Online Training in Hyderabad is designed to help students and working professionals:

Develop practical knowledge of R programming.
Understand statistical analysis and data interpretation.
Learn data exploration and visualization techniques.
Gain knowledge of predictive and descriptive analytics.
Understand how Data Science supports business decision-making.
Build a foundation for pursuing Data Science and data analytics career opportunities.

Our training approach focuses on developing technical knowledge and analytical skills that learners can apply in professional environments.

Who Can Enroll in Our Data Science Course?

Our Data Science courses in Hyderabad are suitable for individuals from different educational and professional backgrounds who want to develop skills in data analytics.

This course is beneficial for:

Graduates: Students who want to start a career in Data Science or data analytics.
IT Professionals: Professionals looking to expand their technical skills and transition into analytics.
Software Developers: Developers interested in learning R programming, statistical analysis, and data-driven applications.
Data Analysts: Professionals who want to strengthen their data analysis and visualization skills.
Business Analysts: Individuals interested in using analytical techniques to support business decisions.
Working Professionals: Experienced professionals who want to apply Data Science techniques in their respective industries.

Prerequisites for Data Science Online Classes

There are no mandatory prerequisites for enrolling in our Data Science online classes. Beginners who are interested in learning data analytics can start with this course.

Basic computer knowledge and an interest in mathematics, statistics, and programming can be helpful. However, prior experience in R programming or Data Science is not compulsory.

Our Data Science Online Training in Hyderabad is suitable for beginners as well as professionals looking to strengthen their analytical knowledge.

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Start Your Data Science Learning Journey with Cyber Eagle

Build your analytical and technical skills with Cyber Eagle Educational Services Pvt. Ltd. Our Data Science online course introduces you to R programming, data visualization, statistical analysis, and predictive analytics.

If you are exploring Data Science courses in Hyderabad or looking for flexible data analysis courses online, our training program can help you develop the foundational skills needed to move forward in this field.

Enroll in Data Science Online Training in Hyderabad at Cyber Eagle and take the next step toward building your career in Data Science and analytics.

Contact Cyber Eagle Educational Services Pvt. Ltd. today to learn more about course details, training schedules, fees, and enrollment.

Frequently Asked Questions

1. What is Data Science Online Training in Hyderabad?

Data Science Online Training in Hyderabad helps students and working professionals learn data analytics, R programming, statistical analysis, data visualization, and predictive analytics. Cyber Eagle Educational Services Pvt. Ltd. offers training designed to help learners develop essential Data Science skills and understand real-world data analysis techniques.

2. Who can enroll in Data Science courses in Hyderabad?

Data Science courses in Hyderabad are suitable for graduates, IT professionals, software developers, business analysts, data analysts, and working professionals interested in building a career in analytics. Beginners with an interest in programming and data analysis can also enroll.

3. What topics are covered in the Data Science online course?

The Data Science online course covers R programming fundamentals, data exploration, data visualization, statistical analysis, descriptive analytics, predictive analytics, data structures in R, R packages, data import and export, cluster analysis, and forecasting techniques.

4. Do I need programming knowledge to join Data Science online classes?

No prior programming experience is mandatory to join Data Science online classes at Cyber Eagle. Beginners can start learning R programming from the fundamentals. Basic computer knowledge and an interest in mathematics and statistics can be helpful.

5. What is the difference between Data Science and Data Analytics?

Data Analytics primarily focuses on examining existing data to identify patterns, trends, and insights. Data Science is a broader field that combines programming, statistics, machine learning, and analytical techniques to solve complex problems and make predictions using data.

6. Will I receive a Data Analyst Certificate after completing the course?

The availability of a Data Analyst Certificate depends on the certification offered by the training institute. Contact Cyber Eagle Educational Services Pvt. Ltd. to confirm the course completion certificate details, eligibility, and certification process.

7. What are the benefits of Data Science Certification?

A Data Science Certification helps demonstrate your understanding of data analytics, programming, statistical methods, and data visualization. It can strengthen your professional profile and support your efforts to explore career opportunities in Data Science, business analytics, and data analysis.

8. Are data analysis courses online suitable for beginners?

Yes, data analysis courses online can be suitable for beginners who want to develop analytical and technical skills. Cyber Eagle's Data Science training with R introduces fundamental concepts, programming techniques, and statistical methods to help learners build a foundation in data analytics.

9. What career opportunities are available after Data Science Training?

After completing Data Science Training and developing relevant practical skills, learners can explore career opportunities such as Data Analyst, Junior Data Scientist, Business Analyst, Data Analytics Associate, and Reporting Analyst. Career opportunities depend on individual skills, experience, and additional qualifications.

10. How can I enroll in Data Science Online Training in Hyderabad at Cyber Eagle?

To enroll in Data Science Online Training in Hyderabad at Cyber Eagle Educational Services Pvt. Ltd., contact the training team to learn about course fees, upcoming batches, online training schedules, curriculum, and certification details. You can call +91 9121462111 or +91 9121459111, or email info.cybereagle@gmail.com for enrollment assistance.