IBM Certified Data Science Webinar/Demo/Batch details
|Session Type||Date & Time|
|New Batch – Weekdays (Offline)||September 22nd at 10AM|
|New Batch – Weekdays (Online)||September 15th at 8AM|
IBM Certified Data Science Course Curriculum (Syllabus)
Module 1: Python Core and Advanced
- What is Python?
- Why does Data Science require Python?
- Installation of Anaconda
- Understanding Jupyter Notebook
- Basic commands in Jupyter Notebook
- Understanding Python Syntax
Data Types and Data Structures
- Variables and Strings
- Lists, Sets, Tuples, and Dictionaries
Control Flow and Conditional Statements
- Conditional Operators, Arithmetic Operators, and Logical Operators
- If, Elif and Else Statements
- While Loops
- For Loops
- Nested Loops and List and Dictionary Comprehensions
- What is function and types of functions
- Code optimization and argument functions
- Lambda Functions
- Map, Filter, and Reduce
- Create, Read, Write files and Operations in File Handling
- Errors and Exception Handling
Class and Objects
- Create a class
- Create an object
- The __init__()
- Modifying Objects
- Object Methods
- Modify the Object Properties
- Delete Object
- Pass Statements
Module 2: Data Analysis in Python
Numpy – NUMERICAL PYTHON
- Introduction to Array
- Creation and Printing of an array
- Basic Operations in Numpy
- Mathematical Functions of 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
- Group by, Pivot table, and Cross Tab
- Concatenating and Merging Joining
- Descriptive Statistics
- Removing Duplicates
- String Manipulation
- Missing Data Handling
Data Visualization using Matplotlib and Pandas
- Introduction to Matplotlib
- Basic Plotting
- Properties of plotting
- About Subplots
- Line plots
- Pie chart and Bar Graph
- Box and Violin Plots
Case Study on Exploratory Data Analysis (EDA) and Visualizations
- 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.
UNSTRUCTURED DATA PROCESSING
- Structured Data and Unstructured Data
- Literals and Meta Characters
- How to Regular Expressions using Pandas?
- Inbuilt Methods
- Pattern Matching
PROJECT ON WEB SCRAPING: DATA MINING and EXPLORATORY DATA ANALYSIS
- Data Mining (WEB – SCRAPING)
This project starts completely from scratch which involves the 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 the 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
Data Types and Data Structures
- Statistics in Data science:
- What is Statistics?
- How is Statistics used in Data Science?
- Population and Sample
- Parameter and Statistic
- Variable and its types
Data Gathering Techniques
- Data types
- Data Collection Techniques
- Sampling Techniques:
- Convenience Sampling, Simple Random Sampling
- Stratified Sampling, Systematic Sampling, and Cluster Sampling
- What is Univariate and Bi Variate Analysis?
- Measures of Central Tendencies
- Measures of Dispersion
- Skewness and Kurtosis
- Box Plots and Outliers detection
- Covariance and Correlation
- Probability and Limitations
- Discrete Probability Distributions
- Bernoulli, Binomial Distribution, Poisson Distribution
- Continuous Probability Distributions
- Normal Distribution, Standard Normal Distribution
- Sampling variability and Central Limit Theorem
- Confidence Intervals
- Hypothesis Testing
- Z-test, T-test
- Chi-Square Test
- F-Test and ANOVA
Module 4. SQL for Data Science
Introduction to Databases
- Basics of SQL
- DML, DDL, DCL, and Data Types
- Common SQL commands using SELECT, FROM, and WHERE
- Logical Operators in SQL
- SQL Joins
- INNER and OUTER joins to combine data from multiple tables
- RIGHT, LEFT joins to combine data from multiple tables
- Filtering and Sorting
- Advanced filtering using IN, OR, and NOT
- Sorting with GROUP BY and ORDER BY
- SQL Aggregations
- Common Aggregations including COUNT, SUM, MIN, and MAX
- CASE and DATE functions as well as work with NULL values
- Subqueries and Temp Tables
- Subqueries to run multiple queries together
- Temp tables to access a table with more than one query
- SQL Data Cleaning
- Perform Data Cleaning using SQL
Module 5: Machine Learning Supervised Learning
- What Is Machine Learning?
- Supervised Versus Unsupervised Learning
- Regression Versus Classification Problems Assessing Model Accuracy
- 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
- 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
- Why Polynomial Regression
- Creating polynomial linear regression
- Evaluating the metrics
- 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.
- 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
- Kappa Score
- Principle of Naive Bayes Classifier
- Bayes Theorem
- Terminology in Naive Bayes
- Posterior probability
- Prior probability of class
- Types of Naive Bayes Classifier
- Multinomial Naive Bayes
- Bernoulli Naive Bayes and Gaussian Naive Bayes
TREE BASED MODULES
- 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:
- 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?
- 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
- 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.
Module 6: Machine Learning Unsupervised 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
- Centroids and Medoids
- Deciding the optimal value of ‘K’ using Elbow Method
- Linkage Methods
- Divisive and Agglomerative Clustering
- Dendrograms and their interpretation
- Applications of Clustering
- Practical Issues in Clustering
Case Study: A Case Study on clusterings using Python
- Market Basket Analysis
- Metric Support/Confidence/Lift
- Improving Supervised Learning algorithms with clustering
Case Study: A Case Study on association rules using Python
CAPSTONE PROJECT: A project on a use case will challenge the Data Understanding, EDA, Data Processing, and Unsupervised algorithms.
- 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: Deep Learning
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
- Introducing Google Colab
- Tensorflow basic syntax
- Tensorflow Graphs
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: Computer Vision
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
- 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: Natural Language Processing (NLP)
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
- POS Tagger
- Named Entity Recognition(NER)
- POS with NLTK
- TF-IDF with NLTK
- Introduction to sequential models
- Introduction to RNN
- Introduction to LSTM
- LSTM forward pass
- LSTM backdrop through time
- Hands-on Keras LSTM
- Sentiment Analysis
- Sentence generation
- Machine translation
- Advanced LSTM structures
- Keras – machine translation
Module 10: Tableau for Data Science
Tableau for Data Science
- Install Tableau for Desktop 10
- Tableau to Analyze Data
- Connect Tableau to a variety of dataset
- Analyze, Blend, Join and Calculate Data
- Tableau to Visualize Data
- Visualize Data In the form of Various Charts, Plots, and Maps
- Data Hierarchies
- Work with Data Blending in Tableau
- Work with Parameters
- Create Calculated Fields
- Adding Filters and Quick Filters
- Create Interactive Dashboards
- Adding Actions to Dashboards
Languages & Tools covered in IBM Certified Data Science
What are the modes of Data Science course training?
We provide Classroom training on IBM Certified Data Science at Hyderabad for the individuals who believe hand-held training. We teach as per the Indian Standard Time (IST) with In-depth practical Knowledge on each topic in classroom training, 80 – 90 Hrs of Real-time practical training classes. There are different slots available on weekends or weekdays according to your choices. We are also available over the call or mail or direct interaction with the trainer for active learning.
We provide Online IBM Certified Master’s Data Science training for the individuals who are occupied with work and the person who believes in one-one learning. We teach as per the Indian Standard Timings, feasible to you, providing in-depth knowledge of Data Science. We are available round the clock on WhatsApp, emails, or calls for clarifying doubts and instance assistance, also giving lifetime access to self-paced learning.
We provide Self-paced training on IBM Certified Data Science course for individuals who are occupied with work and want to learn in their free time. We are giving lifetime access to self-paced learning. Our Self-paced video duration has 100 – 120 Hrs with Real-time practical sessions and Assignments. We are available round the clock on WhatsApp, Emails, or Calls for clarifying doubts and instance assistance.
We provide IBM Certified Data Science course for corporates by experts, which helps businesses to strengthen and reap huge benefits. We always stay updated and provide training on real-time use cases, which bridges the gap enabling the organization to capitalize on the potential of the employees.
Why Data Science at Innomatics Research Labs?
20+ 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 Internship on our projects & products
Projects and use-cases derived from businesses
30+ POCs and 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% placement assistance
Globally Recognized Certification from IBM
Meet Our Expert Advisors
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
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.
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 is now called data scientists. This most in-demand position, therefore, businesses are in dire need of people who can solve complex challenges and foster growth.
Job opportunities (Careers) in Data Science Technology
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 processes 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.
A few reviews from our students
Frequently Asked Questions (FAQs)
1) 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.
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 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
6) Do you provide any placements if i learn Data Science course at Innomatics training institute?
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 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.
8) 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.