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Data Science Training Masters Program
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Embark on a journey into the realm of data science, , as you enroll in our Data Science Training Masters program, meticulously crafted by industry luminaries. Unveil the intricacies of data science principles and practices, traversing a comprehensive curriculum that encompasses designing and implementing machine learning models, harnessing the prowess of Python for data analysis, and crafting data-driven decisions that steer organizations towards success. With our all-encompassing training, participants will acquire the acumen to engineer sophisticated data science solutions, meticulously tailored to address the evolving needs of today’s organizations. Join us on this transformative odyssey,and transmute into a globally recognized data science virtuoso.
Data Science Training Syllabus
Python Statistics for Data Science Course SELF PACED
The course on Python Statistics for Data Science aims to equip students with a thorough grasp of conducting statistical analysis and utilizing data for informed choices. Through interactive modules and practical assignments, participants will master skills like hypothesis testing and regression analysis. This program is well-suited for individuals seeking to improve their data science expertise and develop a stronger comprehension of statistics. It imparts the essential knowledge to excel in the fast-evolving realm of data science.
Course Content
Module Objective: This module focuses on familiarizing you with the concept of data and its diverse types. You will learn to work with sample data, extracting valuable insights through a range of statistical measures.
By the end of this module, you will achieve the following:
- Grasp different data categories
- Acquire knowledge about various types of variables
- Recognize the utility of distinct variable types
- Comprehend the distinction between Population and Sample
- Explore methods of sampling
- Gain insight into Data representation techniques
Topics Covered:
- Introduction to Data Type Varieties
- Employment of Numerical Parameters for Data Representation
- Mean, Mode, Median Calculation
- Analyzing Sensitivity
- Exploring Information Gain
- Understanding Entropy
- Usage of Statistical Parameters in Data Representation
Practical Component:
- Practical demonstration and hands-on experience in Python
- Estimation of Mean, Median, and Mode through Python
- Application of Python for Information Gain and Entropy Calculations
Module Objective: This module aims to provide you with a comprehensive understanding of probability and its practical applications in solving real-world problems. It will also emphasize the significance of Bayesian Inference as a powerful probabilistic tool.
By the end of this module, you will be able to achieve the following:
- Grasp the fundamental principles of probability
- Distinguish between dependent and independent events
- Apply Bayes Theorem to compute conditional, marginal, and joint probabilities
- Explore the concept of probability distribution
- Explain the Central Limit Theorem and its implications
Topics Covered:
- Various Applications of Probability
- Necessity of Probability in Real-World Context
- In-depth Exploration of Bayesian Inference
- Understanding Density Concepts
- Insights into the Normal Distribution Curve
Practical Component:
- Hands-on exercises and demonstrations using Python
- Calculation of probabilities utilizing Python
- Practical implementation of Conditional, Joint, and Marginal Probability in Python
- Visualization of a Normal Distribution Curve through plotting techniques
Module Objective: The focus is to enable you to extract insights from existing data and develop predictive models by employing various inferential parameters as constraints.
By the end of this module, you will be capable of achieving the following:
- Grasp the essence of point estimation through confidence margins
- Derive significant inferences utilizing the margin of error
- Explore the realm of hypothesis testing along with its distinct levels
Topics Covered:
- Conceptual Understanding of Point Estimation
- Application of Confidence Margin
- In-depth Exploration of Hypothesis Testing
- Different Levels of Hypothesis Testing
Practical Component:
- Hands-on engagement and demonstrations facilitated using Python
- Calculation and generalization of point estimates with Python
- Estimation of Confidence Intervals and Margin of Error through practical implementation
Module Objective: This module is designed to acquaint you with diverse methodologies for testing alternative hypotheses.
Upon completion of this module, you will be adept at achieving the following:
- Comprehend the concepts of Parametric and Non-parametric Testing
- Familiarize yourself with different types of parametric tests
- Engage in discussions about experimental design
- Gain an understanding of A/B testing and its implementation
Topics Covered:
- Exploring Parametric Testing
- Varieties of Parametric Tests
- Introduction to Non-Parametric Testing
- Insights into Experimental Design
- Understanding A/B Testing
Practical Component:
- Hands-on exercises and demonstrations carried out using Python
- Application of p-tests and t-tests in Python
- Practical execution of A/B testing using Python
Module Objective: This module serves as an initiation into Clustering, a fundamental aspect of machine learning.
Upon module completion, you will be capable of the following:
- Grasping the essence of association and dependence
- Clarifying the distinctions between causation and correlation
- Understanding the concept of covariance
- Discussing Simpson’s Paradox and its implications
- Illustrating various Clustering Techniques
Topics Covered:
- Exploring Association and Dependence
- Demystifying Causation and Correlation
- Covariance: A Conceptual Understanding
- Unveiling Simpson’s Paradox
- Introduction to Clustering Techniques
Practical Component:
- Hands-on demonstrations and exercises facilitated using Python
- Implementation of Correlation and Covariance in Python
- Practical engagement in Hierarchical Clustering using Python
- Application of K Means Clustering using Python
Module Objective: This module aims to provide you with a solid foundation in Regression Modelling using statistical techniques.
Upon completing this module, you will be proficient in the following:
- Grasping the essence of Linear Regression
- Understanding the intricacies of Logistic Regression
- Implementing Weight of Evidence (WOE) and Information Value (IV)
- Distinguishing between heteroscedasticity and homoscedasticity
- Mastering the concept of residual analysis
Topics Covered:
- Introduction to Logistic and Regression Techniques
- Addressing the Issue of Collinearity
- Exploring Weight of Evidence (WOE) and Information Value (IV)
- Analyzing Residuals
- Differentiating Heteroscedasticity from Homoscedasticity
Practical Component:
- Hands-on demonstrations and exercises conducted using Python
- Practical application of Linear and Logistic Regression in Python
- Utilizing Python to analyze residuals and derive insights
Data Science with Python Certification Course
The Data Science with Python Certification Course holds accreditation from NASSCOM and adheres to industry benchmarks, enjoying the endorsement of the Government of India. This comprehensive course empowers you to attain proficiency in crucial Python principles like data manipulation, file handling, and key Python libraries including Pandas, Numpy, and Matplotlib, all of which constitute vital tools for Data Science. Designed to cater to both novices and professionals, this certification training is tailor-made for jumpstarting your journey into the world of Data Science. Through this program, you will not only gain mastery over Python but also develop a firm grasp of Data Science concepts, paving the way for a successful career in the field.
A. Introduction to Data Science and ML using Python
B. Data Handling, Sequences and File Operations
C. Deep Dive – Functions, OOPs, Modules, Errors, and Exceptions
D. Introduction to NumPy, Pandas, and Matplotlib
E. Data Manipulation
F. Introduction to Machine Learning with Python
G. Supervised Learning – I
H. Dimensionality Reduction
I. Supervised Learning – II
J. Unsupervised Learning
K. Association Rules Mining and Recommendation Systems
L. Reinforcement Learning (Self-Paced)
M. Time Series Analysis (Self-Paced)
N. Model Selection and Boosting
O. Statistical Foundations (Self-Paced)
P. Database Integration with Python (Self-Paced)
Q. Data Connection and Visualization in Tableau (Self-Paced)
R. Advanced Visualizations (Self-Paced)
S. In-Class Project (Self-Paced)
PySpark Certification Training Course
The PySpark certification training program, meticulously crafted by leading industry experts, is tailored to equip you with the essential skills needed to excel as a proficient Spark developer utilizing Python. By enrolling in this PySpark training, you’ll attain mastery over Apache Spark and its comprehensive ecosystem, encompassing crucial components such as Spark RDDs, Spark SQL, Spark Streaming, and Spark MLlib. The curriculum also encompasses the integration of Spark with other indispensable tools like Kafka and Flume.
Delivered through live, instructor-led online sessions, this PySpark training offers hands-on demonstrations to solidify your understanding of pivotal PySpark concepts. Through an immersive learning environment, you will have the opportunity to engage with both the instructor and your fellow learners, fostering a collaborative learning experience.
If you’re aspiring to harness the power of PySpark, accelerate your learning journey by enrolling in this course and benefitting from the expertise of top-rated instructors.
Introduction to Big Data Hadoop and Spark
Introduction to Python for Apache Spark
Functions, OOPs, and Modules in Python
Deep Dive into Apache Spark Framework
Playing with Spark RDDs
DataFrames and Spark SQL
Machine Learning using Spark MLlib
Deep Dive into Spark MLlib
Understanding Apache Kafka and Apache Flume
Apache Spark Streaming – Processing Multiple Batches
Apache Spark Streaming – Data Sources
Implementing an End-to-End Project
Spark GraphX (Self-Paced)
Artificial Intelligence Certification Course
The Advanced Artificial Intelligence Course is designed to empower you with a comprehensive grasp of fundamental text processing techniques, including pivotal concepts such as Tokenization, Stemming, Lemmatization, and POS tagging. Moreover, this course delves into the realm of image preprocessing, image classification, transfer learning, object detection, and computer vision. You will also gain proficiency in implementing renowned algorithms such as CNN, RCNN, RNN, LSTM, and RBM, leveraging the cutting-edge TensorFlow 2.0 package in Python.
This meticulously curated course, developed by industry experts following extensive research, is geared towards meeting the most current industry requisites and trends. By enrolling, you’ll be positioned to harness the potent capabilities of Artificial Intelligence, propelling your career forward and contributing to the global AI revolution. Seize this opportunity to expand your expertise and join the ranks of AI enthusiasts worldwide.
Introduction to Text Mining and NLP
Extracting, Cleaning and Preprocessing Text
Analyzing Sentence Structure
Text Classification-I
Introduction to Deep Learning
Getting Started with TensorFlow 2.0
Convolution Neural Network
Regional CNN
Boltzmann Machine & Autoencoder
Boltzmann Machine & Autoencoder
Emotion and Gender Detection (Self-paced)
Introduction RNN and GRU (Self-paced)
LSTM (Self-paced)
Auto Image Captioning Using CNN LSTM (Self-paced)
Developing a Criminal Identification and Detection Application Using OpenCV (Self-paced)
TensorFlow for Deployment (Self-paced)
Text Classification-II (Self-paced)
In Class Project (Self-paced)
Tableau Certification Training Course
The Tableau Certification Training Course offers a comprehensive curriculum designed to foster expertise in Business Intelligence, Data Visualization, and reporting strategies. This program encompasses a range of essential topics, including Tableau Prep Builder, Tableau Desktop, various charting techniques, Level of Detail (LOD) expressions, and the application of Tableau Online. Through real-world scenarios within industries such as Retail, Entertainment, Transportation, and Life Sciences, participants gain practical exposure to crafting impactful data visualizations.
This course is tailored to enhance career prospects within the rapidly expanding fields of data visualization and Business Intelligence. By equipping learners with essential skills and knowledge, it prepares them to excel in these domains and positions them to excel in Tableau certification examinations. By enrolling in this program, you’re setting yourself up for success and growth within the dynamic landscape of data visualization and BI.
- Data Preparation using Tableau Prep
- Data Connection with Tableau Desktop
- Basic Visual Analytics
- Calculations in Tableau
- Advanced Visual Analytics
- Level Of Detail (LOD) Expressions in Tableau
- Geographic Visualizations in Tableau
- Advanced Charts in Tableau
- Dashboards and Stories
- Get Industry Ready
- Exploring Tableau Online
- In-class Project
- SEP 23, 2023
MON - FRI (6.5 Week)
08 : 30 PM TO 10: 30 PM
Original price was: $1,299.00.$1,199.00Current price is: $1,199.00.
Free Elective Courses along with learning path
Scala Essentials
- At the end of this Module, you should be able to understand the basic concepts related to Scala as a programming language, and learn about basic Scala operations.
Python Programming Certification Course
- In this module, you will Learn about Data Visualization and Introduction to Tableau.
SQL Essentials Training
-
Goal: Data Definition Commands defines the syntax of a database and manipulate database objects.
R Programming Certification Training
- Give an introduction to R programming and different data types in R.
R Statistics for Data Science Course
- Goal: You’ll be introduced to the concept of data and data types, and you’ll be asked to sample data and extract information from it in terms of various statistical parameters.
Capstone Project
Data Science Master Program Capstone Project
To build a predictive model for auto insurance claim initiation, follow these steps:
Data Collection: Gather historical auto insurance data, including owner details and past claims.
Feature Selection: Choose relevant features like owner demographics, claims history, and vehicle information.
Data Splitting: Divide the data into training and testing sets for model development and evaluation.
Model Choice: Select a suitable algorithm like Logistic Regression, Random Forest, or Gradient Boosting.
Model Training: Train the chosen model on the training data to learn patterns.
Evaluation: Assess the model’s performance using metrics like accuracy, precision, and recall.
Fine-tuning: Adjust model parameters for optimal results.
Deployment: Deploy the model to predict new insurance claims.
Monitoring: Continuously monitor and update the model’s predictions as needed.
Remember ethical considerations when using predictive models in insurance.
Job Outlook
Data Science Training FAQ's
Data science is the interdisciplinary field that employs scientific and computational techniques to extract knowledge and insights from data. This practice combines statistics, mathematics, and computer science to scrutinize and interpret data.
Its utility lies in uncovering valuable insights and forecasts from extensive datasets, aiding decision-making across various domains like healthcare, finance, marketing, and social media. This involves handling large and intricate datasets, refining and reshaping data, deploying statistical methods and machine learning algorithms to construct models, and subsequently employing these models to glean insights and predictions.
These insights have the potential to optimize operations, enhance efficiency, and foster informed choices for businesses and organizations. For instance, in healthcare, data science predicts disease outbreaks and individualizes treatments. In finance, it identifies fraud, anticipates market trends, and optimizes investments. In marketing, it tailors user experiences and fine-tunes marketing campaigns.
Ultimately, data science is a potent tool for extracting valuable knowledge from data. As the volume of data grows, its applications continue to expand, establishing it as a crucial asset across industries
A data scientist is a professional who utilizes a blend of expertise in statistics, programming, and machine learning to dissect intricate data sets. Their role encompasses identifying patterns, constructing predictive models, and extracting insights to guide business decisions.
Data scientists usually handle the collection, refinement, and organization of extensive data from diverse sources. They then employ statistical and machine learning methods to analyze this data. Additionally, they might create data visualization tools and dashboards to facilitate comprehension and interpretation by stakeholders.
This role is in high demand across industries such as finance, healthcare, retail, and technology. Data scientists commonly possess advanced knowledge in statistics, computer science, or related fields. Proficiency in programming languages like Python or R, coupled with familiarity in machine learning algorithms and techniques, is pivotal to their work.
Becoming a data scientist can be motivated by several factors:
High Demand: The strong demand for data scientists across diverse industries translates into ample job prospects for those possessing the right skills and experience.
Competitive Salary: Data scientists often enjoy attractive remuneration due to their specialized skills, frequently surpassing average salaries in other professions.
Intellectual Challenge: Working with intricate datasets to extract insights and address real-world issues offers intellectually stimulating and gratifying experiences.
Growth Opportunities: The dynamic nature of data science, with new technologies and methods consistently emerging, offers numerous avenues for professional growth and advancement.
Impactful Contributions: Data scientists wield the potential to effect significant change by leveraging data to solve vital business challenges, enhance products and services, and shape strategic decisions. This capacity for meaningful impact can be deeply fulfilling
A data scientist is a professional who utilizes a blend of expertise in statistics, programming, and machine learning to dissect intricate data sets. Their role encompasses identifying patterns, constructing predictive models, and extracting insights to guide business decisions.
Data scientists usually handle the collection, refinement, and organization of extensive data from diverse sources. They then employ statistical and machine learning methods to analyze this data. Additionally, they might create data visualization tools and dashboards to facilitate comprehension and interpretation by stakeholders.
This role is in high demand across industries such as finance, healthcare, retail, and technology. Data scientists commonly possess advanced knowledge in statistics, computer science, or related fields. Proficiency in programming languages like Python or R, coupled with familiarity in machine learning algorithms and techniques, is pivotal to their work.
We are dedicated to providing you with a comprehensive grasp of Data Science, encompassing a wide spectrum of topics essential for becoming a skilled data scientist. While not limited to these, the covered subjects include: Python programming, Statistics, Data Preparation, Data Analysis, Querying Data, Machine Learning, Clustering, Text Processing, Collaborative Filtering, Image Processing, Computer Vision, Spark MLlib, Data Visualization, and much more. Our goal is to ensure you are well-equipped for a holistic understanding of Data Science.
Enrollment in this Masters Program does not require any prerequisites. Whether you’re a seasoned IT professional or an aspiring entrant into the realm of Data Science, this program is meticulously crafted to cater to individuals from diverse professional backgrounds.
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