IBM Certified Data Science course with Free Internship & 100% Placement Assistance

*Data is the fuel of the 21st Century.

This advanced IBM Certified Data Science course in Hyderabad guarantees career transformation. Here’s a one-time opportunity to learn with the best Data Science training in Hyderabad. Gain knowledge of data analytics, tools, and operations for data science certification and meet the massive demand for these skills. It is VILT & ILT training!

Here you will learn to read, analyze, clean, engineer, and present data in a way that promotes the growth of your business. In order to drive data and extract significant results, this Data Science course can help you progress in leaps and bounds. This IBM Certified Data Science training will accelerate your career as it covers relevant topics & pushes you to work on real-time scenarios.

Artificial Intelligence and Machine Learning in Data Science technology are constantly revolutionizing the industry by innovating and solving complex business problems. Innomatics Research Labs is a hub of advanced training in such technologies.

Our principle of holistic development lies in the strong bedrock that believes in the amalgamation of theoretical knowledge along with practical training. This makes us the best Data Science course in Hyderabad.

PREREQUISITES:
The candidate must be pursuing a Bachelor’s degree.
Previous coding experience is an added benefit.

IBM Certified Data Science Course Curriculum (Syllabus)

Your Title Goes Here

Your content goes here. Edit or remove this text inline or in the module Content settings. You can also style every aspect of this content in the module Design settings and even apply custom CSS to this text in the module Advanced settings.

Module 1: Python Core & Advanced

INTRODUCTION

What is Python?
Why does Data Science require Python?
Installation of Anaconda
Understanding Jupyter Notebook
Basic commands in Jupyter Notebook
Understanding Python Syntax
Identifiers and Operators

Data Types and Data Structures

Variables, Data Types, and Strings
Lists, Sets, Tuples, and Dictionaries

Control Flow and Conditional Statements

Conditional Operators, Arithmetic Operators, and Logical Operators
If, Elif and Else Statements
range
While Loops
For Loops
Nested Loops and List and Dictionary Comprehensions

Functions and Modules

What is function and what types of functions
Code optimization and argument functions
Lambda functions
map, filter, and reduce
Manual higher-order functions & nested functions
Importing a module
Namespace & scope of a variable
using help() and dir() aliasing or renaming
Some Important Modules In Python:
math module, random module, datetime, and os module

File Handling

Create, Read, Write files and Operations in File Handling
Errors and Exception Handling

Class and Objects

Create AClass And Object
__init__(), self parameter
Class Properties, Instance Properties & Static Properties
Modifying Object Properties
Delete Object
Pass Statements
4 pillars of oop
Inheritance, Encapsulation,
Polymorphism, & Abstraction
Multiple dispatch & abc modules
Project walk through

Module 2: Data Analysis using Python

Numpy – NUMERICAL PYTHON

Introduction To Array
Creation & Printing Of An Array
Basic Operations In Numpy
Mathematical Functions Of Numpy
Numpy With Images
Advance Numpy Functions
Numpy Vectorization, Vectorization Vs Loops
Descriptive Stats Using Numpy

2. Data Manipulation with Pandas

Series and DataFrames
Data Importing and Exporting through Excel, CSV Files
Data Understanding Operations
Indexing and slicing and More filtering with Conditional Slicing
Groupby, Pivot table and Cross Tab
Concatenating and Merging Joining
Descriptive Statistics
Removing Duplicates
String Manipulation
Date Time Manipulations
Other Forms Of Data
xls, html & json files
json normalization
Missing Data Handling
mcar, mar & mnar
Visualization Of Missing Data
Imputation Of Missing Data Using Pandas
Merges & Joins
Window Functions
Case Study: A Case Study on Data Manipulation with Pandas

DATA VISUALIZATION

Data Visualization using Matplotlib and Pandas

Introduction to Matplotlib
Basic Plotting
Properties of plotting
About Subplots
Line plots
Pie chart and Bar Graph
Histograms
Box and Violin Plots
Scatterplot

Case Study: A Case Study on Data Visualization Using Matplotlib And Seaborn

Exploratory Data Analysis

What is EDA?
Uni – Variate Analysis
Bi-Variate Analysis
More on Seaborn based Plotting Including Pair Plots, Catplot, Heat Maps, Count plot along with matplotlib plots.
Case Study: A Case Study on EDA

UNSTRUCTURED DATA PROCESSING

Regular Expressions

Structured Data and Unstructured Data
Literals and Meta Characters
How to Regular Expressions using Pandas?
Inbuilt Methods
Pattern Matching
Flags

Project On Web Scraping: Data Collection And Exploratory Data Analysis

Data Collection (web – scraping)
This project starts completely from scratch which involves collection of raw data from
different sources and converting the unstructured data to a structured format to
analyze the data for generating insights.
This project covers the main four steps of data science life cycle which involves
- Data Collection
- Data Mining
- Data Preprocessing
- Data Visualization
  Ex: Text, CSV, TSV, Excel Files, Matrices, Images

Module 3: Advanced Statistics

Introduction to Statistics and Data Types

What is Statistics?
How is Statistics used in Data Science?
Population and Sample
Parameter and Statistic
Data Types

Descriptive Statistics

What is Data, Its type and Data Measures.
What is Univariate and BI Variate Analysis?
Measures of Central Tendencies – Mean, Median, & Mode
Measures of Dispersion – Variance, Standard Deviations, Range, & Interquartile Range
Covariance and Correlation
Box Plots and Outliers detection
Skewness and Kurtosis

Probability Distribution

Probability And Limitations
Axioms Of Probability
Conditional Probability
Random Variable
Discrete Probability Distributions – Probability Mass Functions
Bernoulli, Binomial Distribution, Poisson Distribution
Continuous Probability Distributions – Probability Density Functions
Normal Distribution, Standard Normal Distribution

Inferential Statistics

Sampling Variability and Central Limit Theorem
Confidence Intervals
Hypothesis Testing,A/B testing
Z-test, T-test
Chi-Square Test
F-Test and ANOVA

Case Study:A Case Study with Advanced Statistical Tests

Module 4. Data Base (SQL) + Reporting Tool (Power BI)

Introduction to SQL

Data
What is Database
DBMS
RDBMS
SQLvs MYSQL
SQLvs NoSQL
CRUD operations
Pandas vs SQL

Data Exploration and Data Filtering (DQL and OPERATORS)
Cilent Server Architecture
Workbench introduction
Select (retrive)
Data Exploration
Selecting columns
Performing (limit,distinct,aggregation values,indexing and slicing using offset
Data Filtering
Filtering data based of conditions (with all operators(Like,Regexp,Between))

Module 5: Machine Learning - Supervised & Un-Supervised Learning

INTRODUCTION

What Is Machine Learning?
Supervised Versus Unsupervised Learning
Regression Versus Classification Problems Assessing Model Accuracy

REGRESSION TECHNIQUES

Linear Regression

Simple Linear Regression:
Estimating the Coefficients
Assessing the Coefficient Estimates
R Squared and Adjusted R Squared
MSE and RMSE

Multiple Linear Regression

Estimating the Regression Coefficients
OLS Assumptions
Multicollinearity
Feature Selection
Gradient Descent

Evaluating the Metrics of Regression Techniques

Homoscedasticity and Heteroscedasticity of error terms
Residual Analysis
Q-Q Plot
Cook’s distance and Shapiro-Wilk Test
Identifying the line of best fit
Other Considerations in the Regression Model
Qualitative Predictors
Interaction Terms
Non-linear Transformations of the Predictors

Polynomial Regression

Why Polynomial Regression
Creating polynomial linear regression
Evaluating the metrics

Regularization Techniques

Lasso Regularization
Ridge Regularization
ElasticNet Regularization
Case Study on Linear, Multiple Linear Regression, Polynomial, Regression using Python

CAPSTONE PROJECT: A project on a use case will challenge the Data Understanding, EDA, Data Processing, and above Regression Techniques.

CLASSIFICATION TECHNIQUES

Logistic regression

An Overview of Classification
Difference Between Regression and classification Models.
Why Not Linear Regression?
Logistic Regression:
The Logistic Model
Estimating the Regression Coefficients and Making Predictions
Logit and Sigmoid functions
Setting the threshold and understanding decision boundary
Logistic Regression for >2 Response Classes
Evaluation Metrics for Classification Models:
Confusion Matrix
Accuracy and Error rate
TPR and FPR
Precision and Recall, F1 Score
AUC-ROC
Kappa Score

Naive Bayes

Principle of Naive Bayes Classifier
Bayes Theorem
Terminology in Naive Bayes
Posterior probability
Prior probability of class
Likelihood
Types of Naive Bayes Classifier
Multinomial Naive Bayes
Bernoulli Naive Bayes and Gaussian Naive Bayes

TREE BASED MODULES

Decision Trees

Decision Trees (Rule-Based Learning):
Basic Terminology in Decision Tree
Root Node and Terminal Node
Regression Trees and Classification Trees
Trees Versus Linear Models
Advantages and Disadvantages of Trees
Gini Index
Overfitting and Pruning
Stopping Criteria
Accuracy Estimation using Decision Trees

Case Study: A Case Study on Decision Tree using Python

Resampling Methods:
Cross-Validation
The Validation Set Approach Leave-One-Out Cross-Validation
K-Fold Cross-Validation
Bias-Variance Trade-O for K-Fold Cross-Validation

Ensemble Methods in Tree-Based Models

What is Ensemble Learning?
What is Bootstrap Aggregation Classifiers and how does it work?

Random Forest

What is it and how does it work?
Variable selection using Random Forest

Boosting: AdaBoost, Gradient Boosting

What is it and how does it work?
Hyper parameter and Pro’s and Con’s

Case Study: Ensemble Methods – Random Forest Techniques using Python

DISTANCE BASED MODULES

K Nearest Neighbors

K-Nearest Neighbor Algorithm
Eager Vs Lazy learners
How does the KNN algorithm work?
How do you decide the number of neighbors in KNN?
Curse of Dimensionality
Pros and Cons of KNN
How to improve KNN performance

Case Study: A Case Study on KNN using Python

Support Vector Machines

The Maximal Margin Classifier
HyperPlane
Support Vector Classifiers and Support Vector Machines
Hard and Soft Margin Classification
Classification with Non-linear Decision Boundaries
Kernel Trick
Polynomial and Radial
Tuning Hyper parameters for SVM
Gamma, Cost, and Epsilon
SVMs with More than Two Classes

Case Study: A Case Study on SVM using Python

CAPSTONE PROJECT: A project on a use case will challenge the Data Understanding, EDA, Data Processing, and above Classification Techniques.

Your Title Goes Here

Module 6: Deep Learning

Why Unsupervised Learning
How it Different from Supervised Learning
The Challenges of Unsupervised Learning

Principal Components Analysis

Introduction to Dimensionality Reduction and its necessity
What Are Principal Components?
Demonstration of 2D PCA and 3D PCA
Eigen Values, EigenVectors, and Orthogonality
Transforming Eigen values into a new data set
Proportion of variance explained in PCA

Case Study: A Case Study on PCA using Python

K-Means Clustering

Centroids and Medoids
Deciding the optimal value of ‘K’ using Elbow Method
Linkage Methods

Hierarchical Clustering

Divisive and Agglomerative Clustering
Dendrograms and their interpretation
Applications of Clustering
Practical Issues in Clustering

Case Study: A Case Study on clusterings using Python

CAPSTONE PROJECT: A project on a use case will challenge Data Understanding, EDA, Data Processing, and Unsupervised algorithms.

RECOMMENDATION SYSTEMS

What are recommendation engines?
How does a recommendation engine work?
Data collection
Data storage
Filtering the data
Content-based filtering
Collaborative filtering
Cold start problem
Matrix factorization
Building a recommendation engine using matrix factorization
Case Study

Module 7: CNN & Computer Vision

Introduction to Neural Networks

Introduction to Perceptron & History of Neural networks
Activation functions
- a)Sigmoid b)Relu c)Softmax d)Leaky Relu e)Tanh
Gradient Descent
Learning Rate and tuning
Optimization functions
Introduction to Tensorflow
Introduction to Keras
Backpropagation and chain rule
Fully connected layer
Cross entropy
Weight Initialization
Regularization

TensorFlow 2.0

Introducing Google Colab
Tensorflow basic syntax
Tensorflow Graphs
Tensorboard

Artificial Neural Network with Tensorflow

Neural Network for Regression
Neural Network for Classification
Evaluating the ANN
Improving and tuning the ANN
Saving and Restoring Graphs

Module 8: Natural Language Processing

Working with images & CNN Building Blocks

Working with Images_Introduction
Working with Images – Reshaping understanding, size of image understanding pixels Digitization,
Sampling, and Quantization
Working with images – Filtering
Hands-on Python Demo: Working with images
Introduction to Convolutions
2D convolutions for Images
Convolution – Backward
Transposed Convolution and Fully Connected Layer as a Convolution
Pooling: Max Pooling and Other pooling options

CNN Architectures and Transfer Learning

CNN Architectures and LeNet Case Study
Case Study: AlexNet
Case Study: ZFNet and VGGNet
Case Study: GoogleNet
Case Study: ResNet
GPU vs CPU
Transfer Learning Principles and Practice
Hands-on Keras Demo: SVHN Transfer learning from MNIST dataset
Transfer learning Visualization (run package, occlusion experiment)
Hands-on demo T-SNE

Object Detection

CNN’s at Work – Object Detection with region proposals
CNN’s at Work – Object Detection with Yolo and SSD
Hands-on demo- Bounding box regressor
#Need to do a semantic segmentation project

CNN’s at Work – Semantic Segmentation

CNNs at Work – Semantic Segmentation
Semantic Segmentation process
U-Net Architecture for Semantic Segmentation
Hands-on demo – Semantic Segmentation using U-Net
Other variants of Convolutions
Inception and MobileNet models

CNN’s at work- Siamese Network for Metric Learning

Metric Learning
Siamese Network as metric learning
How to train a Neural Network in Siamese way
Hands-on demo – Siamese Network

Module 9: Gen AI

Introduction to Statistical NLP Techniques

Introduction to NLP
Preprocessing, NLP Tokenization, stop words, normalization, Stemming and lemmatization
Preprocessing in NLP Bag of words, TF-IDF as features
Language model probabilistic models, n-gram model, and channel model
Hands-on NLTK

Word Embedding

Word2vec
Golve
POS Tagger
Named Entity Recognition(NER)
POS with NLTK
TF-IDF with NLTK

Sequential Models

Introduction to sequential models
Introduction to RNN
Introduction to LSTM
LSTM forward pass
LSTM backdrop through time
Hands-on Keras LSTM

Applications

Sentiment Analysis
Sentence generation
Machine translation
Advanced LSTM structures
Keras – machine translation
ChatBot

Module 10: Power BI

Introduction To Power Bi

What is Business Intelligence?

Power BI Introduction
Quadrant report
Comparison with other BI tools
Power BI Desktop overview
Power BI workflow
Installation query addressal
Data Import And Visualizations
Data import options in Power BI
Import from Web (hands-on)
Why Visualization?
Visualization types
Data Visualization (Contd.)
Categorical data visualization
Visuals for Filtering
Slicer details and use
Formatting visuals
KPI visuals
Tables and Matix
Power Queries
Power Query Introduction
Data Transformation – its benefits
Queries panel
M Language briefing
Power BI Datatypes
Changing Datatypes of columns
Power Queries (Cond.)
Filtering
Inbuilt column Transformations
Inbuilt row Transformations
Combine Queries
Merge Queries
Power Pivot And Introduction To Dax
Power Pivot
Intro to Data Modelling
Relationship and Cardinality
Relationship view
Calculated Columns vs Measures
DAX Introduction and Syntax

Download Brochure

Languages & Tools covered in IBM Certified Data Science

Why Innomatics Stands Out the Best!

Why innomatics is different from other edtech

Why Data Science at Innomatics Research Labs?

500+ Industry experts from fortune 500 companies
Dedicated In-house data scientist team accessible round the clock
200+ Hours of intensive practical-oriented training
Flexible Online and Classroom training sessions
5+ Parallel Data science batches running currently on both weekdays & weekends
Backup Classes and Access to the Learning Management System (LMS)
One-to-One Mentorship and Free Technical Support
FREE Data science Internshipon our projects & products
Projects and use-cases derived from businesses
30+ POCsand use cases to work, learn, and experiment
Bi-weekly Industry connects from industry experts from various sectors
Opportunity to participate in Meet-ups, Hackathons, and Conferences
Dedicated training programs for NON-IT professionals
100% placementassistance
Globally Recognized Certification from IBM

Why IBM Certified Data Science at Innomatics Research Labs

What is the scope of Certified Data Scientists in India?

Data Science is quoted as the Sexiest Job of the 21st Century – Harvard Business Review

According to the Harvard Business Review, Data Scientist is the sexiest job of the 21st century. Data Science has also topped LinkedIn’s Emerging Jobs List for 3 years in a row.

During the pandemic in 2021, there were around 82,000 job openings globally that required skills in Data Analysis and India witnessed a 45% increase in the adoption of Artificial Intelligence.

Therefore with each passing day, individuals and organizations are embracing digital increasing the market demand for Data scientists. The average salary of a Data Scientist in Hyderabad alone is at around Rs 6 lakhs. An entry-level Data Scientist can earn anything between Rs 5 -6 lakhs. If a candidate is willing to constantly learn, upgrade and upskill themselves, the compensation package may go up to Rs 24 lakhs or crores for that matter.

Job opportunities (Careers) in Data Science Technology

Data Scientists are needed for businesses in every Industry. Even tech giants such as Google, Amazon, Apple, Facebook, Microsoft are constantly in need of Data Science experts who have in-depth knowledge in data extraction, data mining, visualization, and more. Here are some leading careers in Data Science

Business Intelligence Developer

With an average salary of $89,333, they design and develop business strategies for quick decision-making and growth.

Data Scientist

With an average salary of $139,480, they explore, analyze, visualize, and organize data for the companies. They analyze the complex data sets and processes to find patterns for decision making and predicting the business and drive strategies.

Applications Architect

With an average salary of around $134,520, they track applications behavior and applied in the business to analyze the way they interact with the user.

Industry Architect

With an average salary of $126,353, they analyze the business system and optimize accordingly to support the development of updated technologies and system requirements.

Enterprise Architect

With an average salary of $161,323 they work with stakeholders, including management and subject matter experts (SME), to develop a view of an organization’s strategy, information, processes, and IT assets.

Data Architect

With an average salary is $137,630, they build data solutions that can be applied on multiple platforms.

Data Analyst

With an average salary of $83,989, they transform and manipulate large sets of data, which incorporate web analytics tracking and testing.

Data Engineer

With an average salary of $151,498, they perform real-time processing on data that is visualized and stored.

GLIMPSES OF OUR VICTORY – Successfully Placed Innominions

Here are the Success Stories of our Innominions

Frequently Asked Questions (FAQs)

Your Title Goes Here

What will I learn in IBM certified Data Science?

In Data Science, you will learn how to find valuable data, analyze and apply mathematical skills to it to use in business for making great decisions, developing a product, forecasting, and building business strategies.

What is the average salary of a Data Scientist?

Salary of a Data Scientists entirely depends on the skillset. As per the recent reports, on average a Data Scientists earn ₹14,00,000 per year.

Are there any prerequisites to learn the Data Science course?

One need not have any major knowledge in Data Science. A basic understanding of technology is all enough to get started. It is better to possess knowledge of mathematical and communication skills, Python, R, and SAS tools.

What are my takeaways after completion of the Data Science course?

Based on the program you choose, you will get a course completion certificate from Innomatics. Mastery level certification from IBM.

What are the career opportunities in Data Science Technology?

As data has become the never-ending part of this world, businesses need people to work with data for effective business processing. Organizations are ready to recruit and pay top dollars to the right dollars, which can leverage the business.

Here are some of the roles you can find in Data Science

Research Analyst
Data Scientist
Data Analyst
Big Data Analytics Specialist
Business Analyst Consultant / Manager
Data analyst

If I study Data Science course in Hyderabad, is placement guaranteed?

Apart from the training, we do provide placement and career assistance with capstone projects and hands-on training after completing the course successfully. We do offer internship programs, mockup interviews, hackathons to gain more knowledge and explore a wide range of job opportunities.

Will I get any career support after the Data Science training?

All our trainees will have access to the Learning Management System (LMS), where they can get the backup classes and stay updates, 1-1 interviews, continuous updates on placements, and hackathons.

What is the eligibility criteria to learn Data Science course?

Anyone who has a bachelor’s degree, a passion for data science, and little knowledge of it are eligibility criteria for the Data Science Course.