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Data Science Training Course

Get future ready! with Data Science Training in Hyderabad

Months Icon 3 Months
38 Modules
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Key Features

OSACAD is committed to bringing you the best learning experience with high-standard features including

Key Features
Real-time Practice Labs

Learning by doing is what we believe. State-of-the-art labs to facilitate competent training.

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Physical & Virtual Online Classrooms

Providing the flexibility to learn from our classrooms or anywhere you wish considering these turbulent times.

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24/7 Support On Slack

Technical or Technological, we give you assistance for every challenge you face round-the-clock.

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Job Interview & Assistance

Guiding in & out, until you get placed in your dream job.

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Live projects with our industry partners

An inside look & feel at industry environments by handling real-time projects.

Key Features
Internship after course

Opportunity to prove your talent as an intern at our partner firms and rope for permanent jobs.

Why Data Science ?

Why Data Science
Empowering Management and Officers to Make Better Decisions

An experienced data scientist is likely to be a trusted advisor and strategic partner to the organization’s upper management by ensuring that the staff maximizes their analytics capabilities.

Why Data Science
Directing Actions Based on Trends—which in Turn Help to Define Goals

A data scientist examines and explores the organization’s data, after which they recommend and prescribe certain actions that will help improve the institution’s performance, better engage customers, and ultimately increase profitability.

Why Data Science
Challenging the Staff to Adopt Best Practices and Focus on Issues That Matter

One of the responsibilities of a data scientist is to ensure that the staff is familiar and well-versed with the organization’s analytics product

Who is This program for

  • Fresh graduates who wish to make a career in the field of Big Data or Data Science
  • Final year engineering or MBA students who are planning to learn about predictive analytics
  • Business analysts and IT application engineers who want to hone advanced skills for a successful career in the data analytics field.
  • Best suit for people who would like to become professionals such as data analysts, business intelligence engineers
Who is this program


Best-in-class content by leading faculty and industry leaders in the form of videos,
cases and projects, assignments and live sessions.

Description: Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. In this first module we will introduce to the field of Data Science and how it relates to other fields of data like Artificial Intelligence, Machine Learning and Deep Learning.

  • Introduction to Data Science
  • High level view of Data Science, Artificial Intelligence & Machine Learning
  • Subtle differences between Data Science, Machine Learning & Artificial Intelligence
  • Approaches to Machine Learning
  • Terms & Terminologies of Data Science
  • Understanding an end to end Data Science Pipeline, Implementation cycle

Mathematics is very important in the field of data science as concepts within mathematics aid in identifying patterns and assist in creating algorithms. The understanding of various notions of Statistics and Probability Theory are key for the implementation of such algorithms in data science.

  • Linear Algebra
  • Matrices, Matrix Operations
  • Eigen Values, Eigen Vectors
  • Scalar, Vector and Tensors
  • Prior and Posterior Probability
  • Conditional Probability
  • Calculus
  • Differentiation, Gradient and Cost Functions
  • Graph Theory

This module focuses on understanding statistical concepts required for Data Science, Machine Learning and Deep Learning. In this module, you will be introduced to the estimation of various statistical measures of a data set, simulating random distributions, performing hypothesis testing, and building statistical models.

Descriptive Statistics

  • Types of Data (Discrete vs Continuous)
  • Types of Data (Nominal, Ordinal)
  • Measures of Central Tendency (Mean, Median, Mode)
  • Measures of Dispersion (Variance, Standard Deviation)
  • Range, Quartiles, Inter Quartile Ranges
  • Measures of Shape (Skewness and Kurtosis)
  • Tests for Association (Correlation and Regression)
  • Random Variables
  • Probability Distributions
  • Standard Normal Distribution
  • Probability Distribution Function
  • Probability Mass Function
  • Cumulative Distribution Function
  • Inferential Statistics
  • Statistical sampling & Inference
  • Hypothesis Testing
  • Null and Alternate Hypothesis
  • Margin of Error
  • Type I and Type II errors
  • One Sided Hypothesis Test, Two-Sided Hypothesis Test
  • Tests of Inference: Chi-Square, T-test, Analysis of Variance
  • t-value and p-value
  • Confidence Intervals

Python for Data Science

  • Numpy
  • Pandas
  • Matplotlib & Seaborn
  • Jupyter Notebook


NumPy is a Python library that works with arrays when performing scientific computing with Python. Explore how to initialize and load data into arrays and learn about basic array manipulation operations using NumPy.

  • Loading data with Numpy
  • Comparing Numpy with Traditional Lists
  • Numpy Data Types
  • Indexing and Slicing
  • Copies and Views
  • Numerical Operations with Numpy
  • Matrix Operations on Numpy Arrays
  • Aggregations functions
  • Shape Manipulations
  • Broadcasting
  • Statistical operations using Numpy
  • Resize, Reshape, Ravel
  • Image Processing with Numpy


Pandas is a Python library that provides utilities to deal with structured data stored in the form of rows and columns. Discover how to work with series and tabular data, including initialization, population, and manipulation of Pandas Series and DataFrames.

  • Basics of Pandas
  • Loading data with Pandas
  • Series
  • Operations on Series
  • DataFrames and Operations of DataFrames
  • Selection and Slicing of DataFrames
  • Descriptive statistics with Pandas
  • Map, Apply, Iterations on Pandas DataFrame
  • Working with text data
  • Multi Index in Pandas
  • GroupBy Functions
  • Merging, Joining and Concatenating DataFrames
  • Visualization using Pandas

Data Visualization using Matplotlib

Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+

  • Anatomy of Matplotlib figure
  • Plotting Line plots with labels and colors
  • Adding markers to line plots
  • Histogram plots
  • Scatter plots
  • Size, Color and Shape selection in Scatter plots.
  • Applying Legend to Scatter plotsDisplaying multiple plots using subplots
  • Boxplots, scatter_matrix and Pair plots

Data Visualization using Seaborn

  • Seaborn is a data visualization library that provides a high-level interface for drawing graphs. These graphs are able to convey a lot of information, while also being visually appealing.
  • Basic Plotting using Seaborn
  • Violin Plots
  • Box Plots
  • Cat Plots
  • Facet Grid
  • Swarm Plot
  • Pair Plot
  • Bar Plot
  • LM Plot
  • Variations in LM plot using hue, markers, row and col

Exploratory Data Analysis helps in identifying the patterns in the data by using basic statistical methods as well as using visualization tools to displays graphs and charts. With EDA we can assess the distribution of the data and conclude various models to be used.

Pipeline ideas

  • Exploratory Data Analysis
  • Feature Creation
  • Evaluation Measures

Data Analytics Cycle ideas

  • Data Acquisition
  • Data Preparation
  • Data cleaning
  • Data Visualization
  • Plotting
  • Model Planning & Model Building

Data preparation

  • Selection and Removal of Columns
  • Transform
  • Rescale
  • Standardize
  • Normalize
  • Binarize
  • One hot Encoding
  • Imputing
  • Train, Test Splitting

In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. This module on Machine Learning is a deep dive to Supervised, Unsupervised learning and Gaussian / Naive-Bayes methods. Also you will be exposed to different classification, clustering and regression methods.

  • Introduction to Machine Learning
  • Applications of Machine Learning
  • Supervised Machine Learning
  • Classification
  • Regression
  • Unsupervised Machine Learning
  • Reinforcement Learning
  • Latest advances in Machine Learning
  • Model Representation
  • Model Evaluation
  • Hyper Parameter tuning of Machine Learning Models.
  • Evaluation of ML Models.
  • Estimating and Prediction of Machine Learning Models
  • Deployment strategy of ML Models.

Supervised learning is one of the most popular techniques in machine learning. In this module, you will learn about more complicated supervised learning models and how to use them to solve problems.

Classification methods & respective evaluation

  • K Nearest Neighbors
  • Decision Trees
  • Naive Bayes
  • Stochastic Gradient Descent
  • SVM –
  • Linear
  • Non linear
  • Radial Basis Function
  • Random Forest
  • Gradient Boosting Machines
  • XGboost
  • Logistic regression

Ensemble methods

  • Combining models
  • Bagging
  • Boosting
  • Voting
  • Choosing best classification method

Model Tuning

  • Train Test Splitting
  • K-fold cross validation
  • Variance bias tradeoff
  • L1 and L2 norm
  • Overfit, underfit along with learning curves variance bias sensibility using graphs
  • Hyper Parameter Tuning using Grid Search CV

Respective Performance measures

  • Different Errors (MAE, MSE, RMSE)
  • Accuracy, Confusion Matrix, Precision, Recall

Regression is a type of predictive modelling technique which is heavily used to derive the relationship between variables (the dependent and independent variables). This technique finds its usage mostly in forecasting, time series modelling and finding the causal effect relationship between the variables. The module discusses in detail about regression and types of regression and its usage & applicability


  • Linear Regression
  • Variants of Regression
  • Lasso
  • Ridge
  • Multi Linear Regression
  • Logistic Regression (effectively, classification only)
  • Regression Model Improvement
  • Polynomial Regression
  • Random Forest Regression
  • Support Vector Regression
  • Respective Performance measures
  • Different Errors (MAE, MSE, RMSE)
  • Mean Absolute Error
  • Mean Square Error
  • Root Mean Square Error

Unsupervised learning can provide powerful insights on data without the need to annotate examples. In this module, you will learn several different techniques in unsupervised machine learning.


  • K means
  • Hierarchical Clustering

Association Rule Mining

  • Association Rule Mining.
  • Market Basket Analysis using Apriori Algorithm
  • Dimensionality reduction using Principal Component analysis (PCA)

Natural language is essential to human communication, which makes the ability to process it an important one for computers. In this module, you will be introduced to natural language processing and some of the basic tasks.

  • Text Analytics
  • Stemming, Lemmatization and Stop word removal.
  • POS tagging and Named Entity Recognition
  • Bigrams, Ngrams and colocations
  • Term Document Matrix
  • Count Vectorizer
  • Term Frequency and TF-IDF

Advanced Analytics covers various areas like Time series Analysis, ARIMA models, Recommender systems etc.

  • Time series
  • Time series Analysis.
  • ARIMA example
  • Recommender Systems
  • Content Based Recommendation
  • Collaborative Filtering

Reinforcement learning is an area of Machine Learning which takes suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation.

Basic concepts of Reinforcement Learning

  • Action
  • Reward
  • Penalty Mechanism
  • Feedback loop
  • Deep Q Learning

Artificial intelligence (AI) is the ability of a computer program or a machine to think and learn. It is also a field of study which tries to make computers "smart"

Artificial Neural Networks

  • Neural Networks & terminologies
  • Non linearity problem, illustration
  • Perceptron learning
  • Feed Forward Network and Back propagation
  • Gradient Descent

Mathematics of Artificial Neural Networks

  • Gradients
  • Partial derivatives
  • Linear algebra
  • Li
  • LD
  • Eigen vectors
  • Projections
  • Vector quantization

Overview of tools used in Neural Networks

Tensor Flow


Deep learning is part of a broader family of machine learning methods based on the layers used in artificial neural networks. In this module, you’ll deep dive in the concepts of Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Auto Encoders and many more.

Deep Learning

  • Tensorflow & keras installation
  • More elaborate discussion on cost function
  • Measuring accuracy of hypothesis function
  • Role of gradient function in minimizing cost function
  • Explicit discussion of Bayes models
  • Hidden Markov Models (HMM)
  • Optimization basics
  • Sales Prediction of a Gaming company using Neural Networks
  • Build an Image similarity engine.

Deep Learning with Convolutional Neural Nets

  • Architecture of CNN
  • Types of layers in CNN
  • Different Filters and Kernels
  • Building an Image classifier with and without CNN

Recurrent neural nets

  • Fundamental notions & ideas
  • Recurrent neurons
  • Handling variable length sequences
  • Training a sequence classifier
  • Training to predict Time series

Cloud computing is massively growing in importance in the IT sector as more and more companies are eschewing traditional IT and moving applications and business processes to the cloud. This section covers detailed information about how to deploy Data Science models on Cloud environments.


  • Introduction to Cloud Computing
  • Amazon Web Services Preliminaries - S3, EC2, RDS
  • Big data processing on AWS using Elastic Map Reduce (EMR)
  • Machine Learning using Amazon Sage Maker
  • Deep Learning on AWS Cloud
  • Natural Language processing using AWS Lex
  • Analytics services on AWS Cloud
  • Data Warehousing on AWS Cloud
Hours of Content
Case Study & Projects
Live Sessions
Coding Assignments
Capstone Projects to Choose From
Tools, Languages & Libraries

Languages and Tools covered

Languages and Tools covered Languages and Tools covered Languages and Tools covered Languages and Tools covered Languages and Tools covered Languages and Tools covered

Hands On Projects

Credit Card Fraud Detection

In this data science project, we will predict the credit card fraud in the transactional dataset using some of the predictive models

Expedia Hotel Recommendations

In this data science project, you will contextualize customer data and predict the likelihood a customer will stay at 100 different hotel groups

Personalized Medicine: Redefining Cancer Treatment

Deep Learning Project using Keras Deep Learning Library to predict the effect of Genetic Variants to enable personalized Medicine


Our training is based on latest cutting-edge infrastructure technology which makes you ready for the industry.Osacad will Present this certificate to students or employee trainees upon successful completion of the course which will encourage and add to trainee’s resume to explore a lot of opportunities beyond position

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Artificial Intelligence which is a global company with headquarters in Chicago, USA. Artificial Intelligence has partnered with GamaSec, a leading Cyber Security product company. Artificial Intelligence is focusing on building Cyber Security awareness and skills in India as it has a good demand in consulting and product support areas. The demand for which is predicted to grow exponentially in the next 3 years. The Artificial Intelligence training programs are conducted by individuals who have in depth domain experience. These training sessions will equip you with the fundamentalknowledge and skills required to be a professional cyber security consultant.

All graduates of commerce, law, science and engineering who want to build a career in cyber security can take this training.

There are a number of courses, which are either 3 months or 6 months long. To become a cyber security consultant we recommend at least 6 to 9 months of training followed by 6 months of actual project work.During project work you will be working under a mentor and experiencing real life customer scenarios.

You can get started by enrolling yourself. The enrollment can be initiated from this website by clicking on "ENROLL NOW". If you are having questions or difficulties regarding this, you can talk to our counselors and they can help you with the same.

Once you enroll with us you will receive access to our Learning Center. All online classrooms, recordings, assignments, etc. can be accessed here.

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What do you benefit from this programs
  • Master Python, Machine Learning methods, Data Science and Big Data tools and more
  • Learn various advanced machine learning algorithms like KNN, Decision Trees, SVM, Clustering in detail.
  • Learn deep learning techniques, data visualization and how to build and deploy deep learning models.

I’m Interested

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