Certified Online Data Science Training Institute with 100% Placement Assistance
Data Science stream ranks first among the top trending jobs on Linkedin. This gonna be the next revolution in Information Technology as it drives the world. The future is all about Data Science/Artificial Intelligence – Better decisions, better tools, and better life.
Businesses are embracing Data Science every day to add value to every aspect of their operations. Right from analyzing transaction records, storing data and visualizing them for various purposes. This has led to a substantial increase in the demand for Data Scientists who are skilled in technology, apply maths and analyze the business. There is a demand and supply gap which is creating many highly-paid opportunities for Data scientists.
Data Science is considered to be the Most booming career opportunities of the 21st century in every sector – Cancer detection, paralysis detection, fraud and risk detection in banks, behaviour analysis, Industry Automation likewise every domain is going to be transformed with the help of data science. In a competitive edge, companies have to embrace it or else stay out of the game.
Who can Learn this Online Data Science course?
This Data Science course is best for individuals who are looking to transform their careers. People who have the passion to use the data, analyze, visualize and use it for the betterment of the Business and the society. For those mathematics enthusiasts, who can apply maths in real life and solve complex business challenges. This is specifically ideal for the people who are
- Analysts and Software engineers looking for a career shift in the data science stream.
- Freshers who want to start the career as we teach from the basics and gradually build up your skills.
- Individuals who are graduated and working in the Data Science field and looking to upgrade their careers.
Why Learn Data Science?
Advantages of Online Data Science course training?
By getting associated with Innomatics Research Labs, you will be learning it end to end. We don’t believe in short courses which do not leave you anywhere. Our online data science course helps an individual to build a great foundation in Data Science & Analytics by gaining knowledge on industry-standard tools and techniques through practical oriented approach and use cases derived from the businesses. Here at Innomatics, individuals will gain the knowledge and deep understanding of the statistical techniques critical to Data Analysis and analytic models.
Key Features of an Online Data Science course
Flexible timings for working professionals
24/7 Support on the Learning Management System
Flexible Payments with Easy Installments
Online Data Science Course Curriculum
1. Basic Statistics for Data Science
- Introduction to data science
- Statistics for Data Science
- Introduction to Data
- Descriptive Statistics
- What is Python?
- Why Data Science requires Python?
- Installation of Anaconda
- Understanding Jupyter Notebook
- Basic commands in Jupyter Notebook
- Understanding Python Syntax
2. Data Types and Data Structures
3. Control Flow and Conditional Statements
- Conditional Operators, Arithmetic Operators and Logical Operators
- If, Else if and Else Statements
- While Loops
- For Loops
- Nested Loops
- Lambda Functions
- Map, Filter and Reduce
- Modules and Packages
5. File Handling
- Create, Read, Write files
- Operations in File Handling
- Errors and Exception Handling
3. Data Analysis and Visualizations
- Basic Operations in Numpy
- Array Processing
- Data Frames
- Indexing and slicing
- Merging Joining
- Missing Values
- Data Input and Output
- Cross tab
- Introduction to Matplotlib
- Line plots
- Box and Violin Plots
- Visualization with Seaborn
- Data Mining
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 apply Machine Learning and NLP models. 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
4. Advance Statistics
- Discrete Probability Distributions
- Bernouli Distribution
- Binomial Distribution
- Poisson Distribution
- Continuous Probability Distributions
- Normal Distribution
- Standard Normal Distribution
- Hypothesis Testing
- Parametric Tests
- t- Test
- Non-Parametric Tests
- Chi – Square Test
5. Machine Learning – Supervised Learning
- What is Machine Learning?
- Supervised Vs Unsupervised Learning
- Regression Vs Classification Problems
- Simple Linear Regression
- Multiple Linear Regression
- Estimating the Regression Coefficients
- Polynomial Regression
- An Overview of Classification
- Why Not Linear Regression?
- Logistic Regression
- Evaluation Metrics for Classification Models
- A Case Study on Classification using Logistic Regression
6. Tree Based Models
Decision Trees (Rule Based Learning)
- Basic Terminology in Decision Tree
- Root Node and Terminal Node
- Regression Trees
- Classification Trees
- Advantages and Disadvantages of Trees
- Gini Index, Information Gain/Entropy and Reduction in Variance
- Overfitting and Pruning
- A case study on Decision Trees
- The Validation Set Approach Leave-One-Out Cross-Validation
- k-Fold Cross-Validation
- Bias-Variance Trade-Off for k-Fold Cross-Validation
Ensemble Methods in Tree Based Models:
- What is Ensembled Learning?
- What is Bagging and how does it work?
- What is Random Forest and how does it work?
- The Bootstrap
- Variable selection using RandomForest
- A case study on Random Forest
7. Distance Based Learning Methods
Support Vector Machines:
- The Maximal Margin Classifier
- Support Vector Classifiers
- Support Vector Machines
- Hard and Soft Margin Classification
- Classiﬁcation with Non-linear Decision Boundaries
- Kernel Trick
- Linear, Polynomial and Radial
- Tuning Hyper parameters for SVM
- Gamma, Cost and Epsilon
- SVMs with More than Two Classes
- 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?
- Project 2: A Project on Supervised Learning
8. Unsupervised Learning
The Challenges of Unsupervised Learning
- Principal Components Analysis:
- Introduction to Dimensionality Reduction and it’s necessity
- What Are Principal Components?
- Eigen Values, Eigen Vectors and Orthogonality
- Transforming Eigen values into a new data set
- Proportion of variance explained in PCA
- A Case Study on PCA
- Clustering Methods:
- K-Means Clustering
- Centroids and Medoids
- Deciding optimal value of ‘K’ using Elbow Method
- Linkage Methods
- Hierarchical Clustering
- Divisive and Agglomerative Clustering
- Dendrograms and their interpretation
- Applications of Clustering
- A Case Study on Clustering
- Project 3: A Project on Unsupervised Learning
9 Natural Language Processing (NLP)
Introduction to NLP
- What is Natural Language Processing?
- Structured vs Unstructured Data
- Importance and Advantages of Natural Language Processing
- Text Mining
- Text Preprocessing:
- Regular Expressions for Pattern Matching
- Text Normalization
- Text Tokenization
- Sentence Tokenization
- Word Tokenization
- Text Segmentation
- POS Tagging
- Natural Language Understanding:
- Word Embedding’s
- Bag of Words Model
- TF-IDF Vectorizer
- Count Vectorizer
Case Study on Spam Detection
Projects: Extracting unstructured text from files or Websites or any sources
10 Deep Learning
Introduction to Neural Networks
- Introduction to Perceptron
- Activation Functions
- Cost Functions
- Gradient Decent
- Stochastic Gradient Descent
- Back propagation
- Tensorflow Basic Syntax
- Tensorflow Graphs
- Variables and Placeholder
- Saving and Restoring Models
- Building Neural Network with Tensorflow:
- Neural Network for Regression
- Neural Network for Classification
- Evaluating the ANN
- Improving and tuning the ANN
- Building a Convolutional Neural Networks with Keras:
- Introduction to Computer Vision
- OpenCV library in Python
- Convolution Operation
- ReLU Layer
- Softmax and Cross Entropy
- Building a CNN model with Keras
- Training the CNN model
- Saving the CNN model
- Introduction to Computer Vision
Image Processing Using Convolution Neural Network
Why choose Online Data Science at Innomatics Research Labs?
Use-case based Practical Oriented approach
We believe in a hands-on training approach and students can undertake the capstone projects and challenges derived from the real-time business. These projects can be one under the guidance of the industry experts and this provides a great opportunity for the students to learn and build the portfolio for accelerating the career.
Not just training but also Personalized Mentorship
Not just the recorded classroom sessions, we go beyond the online training. Our trainees can get access to the trainee round-the-clock and get assistance whenever needed. As an Alumni of Innomatics, you can attend the Industry connects, workshops, seminars, and meetups conducted by Innomatics Research labs. This helps you to connect with industry experts and also will help you to get insights into how data science is used in the industry.
Dedicate Placement Assistance
Having a dedicated placement verticle and collaborations. As an IBM Authorized training partner, we have access to hundreds of MNCs to which we send our trainees. Thousands of people have chosen us for their career transformation. Apart from CV preparation, mock-up interviews, we will also help you by providing numerous placement opportunities.
Globally recognized certifications from IBM
As an authorized training partner with IBM, we provide individuals with two mastery level certifications provided by IBM.
Not just Learning – Blended to perfection
With us there is everything. An individual can learn, explore and experience from the business cases derived. This would be a mix of self-paced, interactive and applied to learn.
Affordability at ease
We believe that learning should never stop and to make technology accessible for every individual, we provide the entire course (including Deep Learning & Natural Language Processing) at an affordable price.
Key Highlights of Online Data Science Program
120+ Industry experts from fortune 500 companies
Dedicated In-house data scientist team accessible round the clock
200+ Hours of intensive practical oriented training
Data science Internship on our projects & products
30+ POCs and use cases to work, learn and experiment
1000+ professionals trained in last one year
5+ products already developed and ready to be deployed
Bi-weekly Industry connects from industry experts from various sectors
Project and use-cases derived from businesses
Opportunity to participate in meet-ups, hackathons, and conferences
Dedicated training programs for NON-IT professionals
100% placement assistance
Backup Classes and Access to the Learning Management System (LMS)
Flexible Online training sessions
Free Technical Support
Job opportunities in Data Science
Data Scientists are needed for businesses in every Industry. Even fortune companies as Google, Amazon, Apple, Facebook, Microsoft need data science experts who have in-depth knowledge of data extraction, data mining, visualization, etc. Some of the leading data science careers are,
- Business Intelligence Developer
- Data Architect
- Applications Architect
- Industry Architect
- Enterprise Architect
- Data Scientist
- Data Analyst
- Data Engineer
Frequently Asked Questions (FAQs) on Online Data Science Course
Why choose an online data science course?
The best reason one could say to choose online data science course that is specially provided by innomatics is due to the flexibility of it. The online course for data science is so intuitive that you will enjoy it while doing it. Another thing is the convenience of learning at your home with the same quality that could be on-campus.
How are Innomatics verified certificates awarded?
Upon completion of the program, we will conduct the assessment, hackathons, assignments and based on the qualification in the assessment, trainees will be awarded the certification from IBM. This is a globally recognized certificate that will include over top MNCs from around the world.
What are the benefits of Innomatics self-paced Training?
Innomatics offers self-paced training to those who wants to learn at their own pace, This provides includes lifetime access to LMS where the trainees can see the backup classes, one-one sessions and queries through email. There would be an arrangement of virtual live class sessions for the trainees as well.
What do i need to do if I want to switch from self-paced training to instructor-led training?
At Innomatics, the trainee can learn based on their comfort level. One can easily switch from self-paced to online instructor-led training without any extra effort.
What is the other mode so draining available at Innomatics?
Innomatics also provide Classroom Data Science Training, Online Data Science Training and Corporate Training for the employees which can upskill the workforce.
Will there be any support provided if I need assistance on the projects?
Innomatics trainers would round-the-clock and here to provide 100% assistance for all the queries that trainee raise. We are available through email or call and can also arrange a one-one session with the trainer if needed.
Do you provide any placement assistance?
Innomatics help trainees to achieve their dreams by helping trainees finding potential recruiters, resume busking, mockup interviews and helping with the entire recruiting process.
How can I choose the best specialization?
Innomatics will provide the trainees with the options that best suit them based on their background. We would suggest the best based on the role and interests. We will suggest the below based on the roles.
Data Engineering:Software and IT Professionals
Deep Learning: Engineers, Software and IT Professionals
Natural Language Processing: Engineers, Software and IT Professionals
Business Analytics: Engineers, Managers, Marketing and Sales Professionals, Domain Expert
Business Intelligence/ Data Analytics: Engineers, Marketing and Sales Professionals, Freshers