What are the Tools used in Data Science?
What is the scope of Data Scientists in India?
In this Data eruptive field, businesses need a head who owns a brain that is good at maths, finesse, the eyes of an artist, and more.
Data Science is quoted as the Sexiest Job of the 21st Century by Harvard Business Review
There is a severe shortage of Data Scientists with excellent analytical skills and deep quantitative abilities who can analyze big data across all industries. The method of realizing questions, data, product use cases are done by applying their curiosity, quantitative skills, and intellect toward understanding big data are now called data scientists. This most in-demand position, therefore, businesses are in dire need of the people who can solve complex challenges and foster growth.
Data Science Batch details
Data Science New Batch Details
|Weekday Batch (MON-FRI)||04/05/2020||8AM, 10 AM, 7PM|
|Weekend Batch (SAT-SUN)||02/05/2020||10 AM|
|Online Batch||13/04/2020||8AM, 10AM, 7PM|
Current Available Slots
|Timings||Total Seats||Available Seats|
|10AM (MON-FRI)||30 (2 classrooms)||10|
|10AM (SAT & SUN)||30 (2 classrooms)||12|
|ONLINE – 6AM,7PM||20||10|
Note: Demo On Every Saturday & New Batch Will Starts On Every Monday
Data Science Course Curriculum
1. Introduction to Data Science
- Introduction to data science
- Application of Data Science
- Life cycle of data science projects
- Data and its various forms
2. Python - Core, Advanced
- 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, Elif and Else Statements
- While Loops
- For Loops
- Nested Loops
- List and Dictionary Comprehensions
- Code Optimization
- Lambda Functions
- Modules and Packages
5. File Handling
- Create, Read, Write files
- Operations in File Handling
- Errors and Exception Handling
6. Python Datatime
7. Object Oriented Programming(OOP)
- Attributes and Class Keywords
- Constructers and Destructors
- Using Self Parameter
- Class Object attributes and Methods
- Overloading and Over Riding
- Method Overloading
- Operator Overloading
- Super Keyword
- Method Overriding
3. Python for Data Analysis & Visualization
- Basic Operations in Numpy
- Array Processing
- Data Frames
- Indexing and slicing
- Merging Joining
- Missing Values
- Data Input and Output
- Cross tab
10. Data Visualization
- Introduction to Matplotlib
- Line plots
- Box and Violin Plots
- 11. Visualization with Seaborn
4. Understanding Text using Python
12. Regular Expressions
- Literals and Meta Characters
- How to Regular Expressions using Pandas?
- Inbuilt Methods
- Pattern Matching
- 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
5. Statistics - DESCRIPTIVE & INFERENTIAL
Basic Statistics Terminology
- What is Statistics?
- How Statistics is used in Data Science
- What is Probability?
- Population and Sample
- Sampling Techniques
- Convenience Sampling
- Simple Random Sampling
- Systematic Random Sampling
- Stratified Sampling
- Cluster Sampling
- Dependent and Independent Variables
- Qualitative and Quantitative Data
- Categorical Data
- Numerical Data
- Discrete and Continuous Data
- Categorical Data
- Mean, Median and Mode
- Standard Deviation and Variance
- Box Plot and Distribution
Basics of Probability
- Probability vs Statistics
- Probability Rules
- Probability Types
- Marginal Probability
- Joint Probability
- Union Probability
- Conditional Probability
- Conditional Probability
- Bayes Theorem
- Confusion Matrix
- Variance of Distribution
- Discrete Probability Distribution
- Continuous Probability Distribution
- Normal Distribution
- Gaussian Distribution
- Confidence Interval
- Standard Error
- Margin of Error
- Hypothesis Testing
- Chi-square test
6. MACHINE LEARNING – SUPERVISED LEARNING
- What is Machine Learning?
- Difference between Supervised Learning and Unsupervised Learning?
- Difference between Regression and Classification Models?
2. Linear and Multiple Regression
- Relationship between variables: Regression (Linear, Multivariate Linear Regression) in prediction.
- Hands on Linear and Multiple Regression using a use case.
- Understanding the summary output of Linear Regression
- Residual Analysis
- Identifying significant features, feature reduction using AIC, multi-collinearity check, observing influential points, etc.
- Hypothesis testing of Regression Model
- Confidence intervals of Slope
- R-square and goodness of fit
- Influential Observation – Leverage
- Polynomial Regression
- Categorical Variable in Regression
3. Logistic Regression
- Logistic Regression Intuition
- Understanding Logit Function.
- Hands-on Python Session on Logistic Regression using business case.
- Measuring the Evaluation Metrics – Confusion Metrics, Accuracy, Precision, recall and ROC Curve.
4. Navie Bayes Classifier
- Review probability distributions, Joint and conditional probabilities
- Model Assumptions, Probability estimation
- Required data processing
- Feature Selection
5. Principal Compound Analysis (PCA)
- Introduction to dimensionality reduction and it’s necessity
- Background: Eigen values, Eigen vectors, Orthogonality
- Principal components analysis (PCA)
- Feature Extraction
- Advantage and application of Dimensionality reduction.
6. Time Series (Forecasting)
- Trend analysis
- Cyclical and Seasonal analysis
- Smoothing; Moving averages; Auto-correlation; ARIMA
- Application of Time Series in financial markets
7. Decision Tree (Rule – Based)
- Decision nodes and leaf nodes
- Variable Selection, Parent and child nodes branching
- Stopping Criterion
- Tree pruning and Depth of a tree
- Metrics for decision trees-Gini impurity, Information Gain, Variance Reduction
- Regression using decision tree
- Interpretation of a decision tree using If-else
- Pros and cons of a decision tree
- Accuracy estimation using cross-validation
8. K-Nearest Neighbor (Distance Based Learning)
- What is KNN and why do we use it?
- KNN-algorithm and regression
- Curse of dimensionality and brief introduction to dimension reduction
- KNN-outlier treatment and anomaly detection
- Pros and cons of KNN
9. Support Vector Machine (Distance Based Learning)
- Linear learning machines and Kernel space, making kernels and working in feature space
- Hands on example of SVM classification and regression problems using a business case in Python.
10. Esemble Methods
- Introduction to Ensemble
- Bias and Tradeoff
- Bagging & boosting and its impact on bias and variance
- Random forest
- Gradient Boosting
- Predictive Analytics
- Banking Use cases – Customer Service prediction,
- Health care Use cases – Heart Disease, Diabetics
- Insurance Use cases
- Telecom Churn Prediction
- Bike Sharing
- Air Quality
7. MACHINE LEARNING – UNSUPERVISED LEARNING
- Different clustering methods
- review of several distance measures
- Iterative distance-based clustering
- Dealing with continuous, categorical values in K-Means
- Constructing a hierarchical cluster, and density-based clustering.
- Test for stability check of clusters
- Hands-on implementation of each of these methods in Python
2. Recommendation Systems
- Association Rules:
- How to combine clustering and classification;
- A mathematical model for association analysis
- Apriori: Constructs large item sets with mini sup by iterations
- Metrics of rules-Lift, Support, Confidence, Conviction
- Recommendation Rules:
- Collaborative Filters
- Content based Learning
8. Natural Language Processing (NLP) – Text Mining
- What is Text Mining?
- Structured and Unstructured Data
- Extracting Unstructured text from files and websites
2. Text Preprocessing
- Regular Expressions for Pattern Matching
- Text Normalization
- Text Tokenization
- Sentence Tokenization
- Word Tokenization
- Text Segmentation
3. Natural Language Understanding (NLP Statistical)
- Automatic Tagging
- N-grams Tagging
- Transformation based Tagging
- Bag of Words
- POS Tagging
- TF – IDF
- Cosine Similarity
- Thinking about the math behind text; Properties of words; Vector Space Model
- Named Entity Recognition
- Relation Extraction
4. Matrix Factorization
- Singular Value Decomposition
5. Text Indexing
- Inverted Indexes
- Boolean query processing
- Handling phrase queries, proximity queries
- Latent Sematic Analysis
6. Text Classification
- Text Mining
- Sentiment Analysis
- Spam Detection
- Dialogue Prediction
9 Artificial Intelligence
1. Introduction to Neural Networks
- Introduction to Neural Network
- Introduction to Perceptron
- Activation Functions
- Cost Functions
- Gradient Decent
- Stochastic Gradient Descent
- Back propagation
2. Deep Frameworks
- Installing Tensorflow and Keras
- Tensorflow and Keras Basic Syntax
- Tensorflow Graphs
- Variables and Placeholder
- Saving and Restoring Models
3. Artificial Neural Network with Tensorflow
- Neural Network for Regression
- Neural Network for Classification
- Evaluating the ANN
- Improving and tuning the ANN
4. Convolution Neural Networks
- Convolution Operation
- ReLU Layer
- Full Connection
- Softmax and Cross Entropy
5. Building Convolution Neural Network in Python
- Introduction to Computer Vision
- OpenCV library in Python
- Getting Started with Images/Videos
- Operations on Images
- Image Processing in OpenCV
- Geometric Transformation of Images
- Affine Transformation
- Perspective Transformation
- Imaging Thresholding
- Edge Detections
- Morphological Transformation
- Harris Corner Detection
- Geometric Transformation of Images
- Reshaping Images
- Normalizing Images
- Building Convolutional Network with Tensorflow
- Training CNN for Image Classification
- Image Classification
6. Keras (Backend Tensorflow)
- Keras vs Tensorflow
- Introduction to Keras
- Building Artificial Neural Network with Keras
- Building Convolution Neural Network with Keras
7. Natural Processing Language (Sequential Process)
- The Idea behind Recurrent Neural Networks
- Vanishing Gradient Problem
- LSTM (Long Short-Term Memory)
- GRU (Gated Recurrent Unit)
- Face Recognition
Face Recognition project gives details of the person and can recognize the gender and names. This project involves in
- Collection of images
- Preprocessing the data
- Applying the Model (Machine Learning or Deep Learning)
- Training and Testing using the model
Ex: Security Unlock, Gender Recognition, Identity Recognition
Virtual Assistants are now a common requirement for an Organization. But, to make the assistant more effective we are now into the chatbots which involves Natural Language Process, Deep Learning and Artificial Intelligence. This interactive chatbots are designed to serve as an intellectual responsive process.
Ex: Alexa, Siri, Google Assistant
- Creating pickle and frozen files
- Cloud Deploying Machine Learning and Deep Learning model for production
Job opportunities (Careers) 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, and more. Some of the leading data science careers are
- Business Intelligence Developer, with an average salary of $89,333, to design and develop business strategies for quick decision making and growth.
- Data Architect, whose average salary is $137,630, builds data solutions that can be applied on multiple platforms.
- Applications Architect, whose average salary is $134,520, tracks 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, to 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 to 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 Scientist, with an average salary of $139,480, to explore, analyze, visualize, and organize data for the companies. They analyze the complex data sets and process to find patterns for decision making and predicting the business and drive strategies.
- Data Analyst- with an average salary of $83,989 to transform and manipulate large sets of data, which incorporated web analytics tracking and testing.
- Data Engineer – with an average salary of $151,498, to perform real-time processing on data that is visualized and stored.
Meet Our Expert Advisors
Why Data Science at Innomatics Research Labs?
- 18+ Data Science 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
- 200+ professionals trained in the last three months
- 5+ products already developed and ready to be deployed
- Eight parallel Data science batches running currently on both weekdays and weekends
- 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
- New batch on every Monday and Saturday for working and non-working professionals
- 100% placement assistance
A few reviews from our students
Frequently Asked Questions (FAQs)
1) What will I learn in 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.
2) 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.
3) 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.
4) 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.
5) What are the career opportunities in Data Science?
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
6) Do you provide any placements if I learn the Data Science course at Innomatics?
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.
7) Will I get any career support after the 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.
8) What is the eligibility criteria to learn the 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.