Data Science Course in Trivandrum - Become a Certified Data Scientist
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Overview of Data science and Machine Learning Course in Trivandrum
Entri Elevate Data Science Course provides a thorough overview of analytics, machine learning, and data visualization. Our course covers essential Python programming, statistical analysis techniques, and hands-on projects to enhance practical understanding. With real-world case studies and expert guidance, students gain proficiency in applying data science to solve complex problems. Whether you're a beginner or seeking advanced knowledge, Entri ensures you develop the expertise needed to thrive in the rapidly evolving field of data science.
Inclusive & Immersive Hybrid Training Sessions
Industry Expert Sessions
80+ Live & Recorded Sessions
Soft Skill Sessions
Industry Networking
Placement Training
Illinois Tech Certification
Data Science Skills Covered
By completing a Data Science course, you can acquire skills such as:
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Tools Covered
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Data Science Job Roles
What Exactly Does a Data Scientist Do? Lets Dive Deep into Roles!
Data Scientist
A data scientist collects, analyzes, and interprets large sets of data to identify patterns and trends. They use various tools and techniques to extract insights and make data-driven decisions. With a data science and machine learning course, individuals can acquire skills in data mining, programming languages, and statistical analysis, making them suitable for this role.
Machine Learning Engineer
Machine learning engineers work on developing algorithms and models that enable machines to learn from data and make accurate predictions. These professionals have a strong background in mathematics, programming, and data science. A course in data science and machine learning provides individuals with a solid foundation in these areas, making them well-equipped for this role.
Data Analyst
Data analysts gather, organize, and interpret data to provide valuable insights to businesses. They use statistical tools and techniques to identify trends and patterns and communicate their findings to key stakeholders. A data science and machine learning course equips individuals with skills in data manipulation, visualization, and analytics, making them a perfect fit for this role.
Business Intelligence Analyst
Business intelligence analysts are responsible for studying data to uncover trends and patterns that can assist companies in making informed decisions. They employ a variety of data analytics tools to collect information from various data sources and present it in a format that can be easily comprehended by stakeholders. This position necessitates strong analytical abilities, business expertise, and excellent communication skills.
Curriculum
The curriculum is designed to help you learn the skills required to become a successful professional.
- Introduction to Programming
- What is programming?
- Compiler,Interpreter
- Source Code
- Machine code
- Algorithms
- Editors
- Introduction to Data Science
- What is Data Science?
- Job Roles
- Terminologies
- Data Science Applications and its work flows
- Language Introduction and Installation
- Python history
- Python features
- python and pycharm installation.
- Python Basics
- Print command
- Comments,escape sequences
- Variables
- Data types
- User interactive command, operators
- Conditional and looping statements
- Selection statements
- Control statements
- Break and continue statements
- Nested loops
- Data Structures in python
Introduction to user defined data structures and non-primitive data structures:list,
dictionaries, set,tuples, strings and sequences,accessing and modifying elements in data
structures,comprehension: list, set and dictionary.
- Functions in Python
Defining functions, passing arguments to functions, different types of arguments, returning
values from functions, local and global namespace, lambda function, recursion, filter,
map,reduce, eval. Generators and decorators.
- File Handling and Exception Handling
File processing, Reading and writing files using ‘with’ statements. What is an Exception?,
raising and catching exceptions and handling errors gracefully using try-catch-finally.
- Object Oriented Programming
Introduction to OOPs, Classes and objects, inheritance and polymorphism, encapsulation
and abstraction.
- Modules in Python
Introduction to modules, importing modules, creating and using modules.
- Regular Expressions
Defining regular expressions, using regular expressions with python.
10.Pandas and Numpy
Introduction to Pandas library, reading and writing data with pandas, data cleaning and
exploration with pandas. Introduction to numpy, numpy basic operation.
- Data Visualization with Matplotlib and seaborn
Introduction, basic and advanced plotting technique
- Introduction to SQL
- Introduction to databases and database management system
- Overview of MySQL
- Installing and getting started with MySQL workbench.
- SQL Database
- Creating databases
- Dropping databases
- Introduction to tables
- Data types in MySQL
- Data Definition Language
- Introduction DDL commands
- Creating table
- Modifying table using ALTER, DROP, TRUNCATE, RENAME
- Data Manipulation Language
- Overview of MySQL SELECT statement
- Retrieving data
- Filtering and sorting
- Joining and combining data
- Updating and deleting data
- Inserting data
- Constraints(PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL.
- MySQL Functions and Joins
MySql built-in functions, inner, outer and self joins and combining data from multiple tables.
- MySQL subqueries and views
Subqueries and nested queries,creating and using views, normalization and denormalization
- MySQL stored procedures and triggers
Creating and using stored procedures and triggers, database security and user management.
Project
Build a simple database to help us manage the booking process of a sports complex. The sports complex has the following facilities:
- 2 tennis courts
- 2 badminton courts,
- 2 multi-purpose fields
- 1 archery range.
Each facility can be booked for a duration of one hour. Only registered users are allowed to make a booking. After booking, the complex allows users to cancel their bookings latest by the day prior to the booked date. Cancellation is free. However, if this is the third (or more) consecutive cancellations, the complex imposes a $10 fine
- PowerBI Introduction
- Introduction
- PowerBI
- Power query editor
- PowerBI service,
- Installation and configuration of powerBI desktop
- Connecting to data sources and loading data
- Data modeling in PowerBI
Understanding data modeling and relationships creating relationships between tables, building hierarchies and calculated columns, transforming using power query.
- Data Visualization in PowerBI
Understanding data visualization best practices, building basic visualizations(charts, graphs, tables etc..), formatting and customizing visualizations.
- Building Interactive reports
Using filters and slicers to create interactive reports, building drill-through reports and drill-down visualizations, creating bookmarks and scenarios.
- Creating Dashboards
Building dashboards with multiple visualizations, creating dashboards tiles and adding images, creating custom visuals using powerBI
- Data Analytics with PowerBI
Understanding data analytics and DAX language, creating DAX formulas and measures.
- Project
Financial Performance analysis: to optimize financial reporting in a firm that provides customers to track their financial health and productivity. Create data models and visualizations, and dashboards and present the project results and insights.
- Probability & Statistics
Definition of probability,conditional probability,independent events, Bayes' rule, Random variables, discrete random variable, continuous random variable, probability density function, mean, median, mode, Standard deviation, correlation, correlation coefficient. Testing of hypothesis, confidence interval, Chi-squared test, t-test
- Introduction to Machine Learning
Machine learning concepts and applications,Overview of Supervised, Unsupervised, and Reinforcement learning.Exploring common machine learning algorithms
- Data Preprocessing for Machine Learning
Types of Data: Categorical and Numerical Data, Understanding data preprocessing and its importance in machine learning, Handling missing data, outliers, and categorical variables, Label Encoding, Feature scaling and normalization techniques.Python Library: Numpy, Pandas, Sklearn
- Project
Use a dataset then remove NaN values and apply label encoder and scaling methods.
- Supervised Learning : Regression
Understanding regression analysis and its use in machine learning, Building linear regression models,
Evaluating model performance and making predictions.
- Project using Regression
Create a model using Linear regression algorithms and predict using the best algorithm among them.
- Supervised Learning
Classification Understanding classification analysis and its use in machine learning, Building logistic regression, Support Vector Machines, Random Forest, K-Nearest Neighbor, Naïve Bayes and Decision tree models. Evaluating model performance using different classification accuracy metrics and making predictions.
- Project
Create a model using different algorithms for a given dataset and choose the best algorithm among them.
- Unsupervised Learning
Clustering Understanding clustering analysis and its use in machine learning, Building k-means and hierarchical clustering models,Evaluating model performance and making predictions,
- Project
Create a model using different algorithms and predict using the best algorithm among them.
- Unsupervised Learning
Dimensionality Reduction Understanding dimensionality reduction and its use in machine learning, Building principal component analysis (PCA) and t-SNE models, Evaluating model performance and making predictions.
- Natural Language Processing
Basic concept of NLP, Data Cleaning: remove punctuations, tokenization, remove stop words, stemming, lemmatization.Packages of NLP : NLTK (Natural Language ToolKit), Pattern, TextBlob,Vectorization techniques: Bag of Words, TF-IDF
- Project
For the given paragraph do the following: word_tokenise and sent_tokenise, using stop words eliminate most common words and do stemming and lemmatization.
Deep learning basics: Neural Network, perceptron,Back-Propagation, Activation functions, Deep networks, Regularization, Dropout, Batch Normalization.Python libraries for Deep learning : Keras, Tensor flow. Convolutional neural networks: Introduction to CNNs, Convolution, Correlation, FIltering. CNN architectures, Compiling and fitting a model Advanced Deep architectures:Recurrent Neural networks (RNNs), Advanced RNN: LSTM
Project
Create a deep network model using CNN and calculate its accuracy and loss values.
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Eligibility /Pre-requisities
Anyone with an interest in data science and machine learning can enroll in these courses. There is no specific educational background required. However, a basic understanding of mathematics, statistics, and programming can be beneficial.
Basic Programming Skills
Proficiency in at least one programming language, commonly Python or R. Basics of programming logic, control structures, functions, and object-oriented programming concepts if using Python.
Logical thinking
Having the ability to think critically and approach problems analytically by identifying its key components, and understanding how they relate to each other.
Problem-Solving Skills
Ability to approach problems analytically and systematically. Understanding of how to apply different data science techniques to solve real-world problems.
Basic Mathematics skills
Basic mathematics including algebra, calculus, and probability theory. Linear algebra is particularly important for understanding many machine learning algorithms.
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Connect employers with a global network of recruitment partners to meet their hiring goals.
Why Live Sessions?
Live sessions on data science and machine learning have several advantages that make them a valuable learning resource.
Interactive Learning
Allow direct interaction between instructors and students,
Hands-on Practice
Allow direct interaction between instructors and students, fostering a dynamic learning environment where questions can be asked and answered in real-time.
Community Engagement
Live sessions often build a sense of community among learners, encouraging peer interaction and collaboration.
Live Clarifications
Students can clarify doubts and receive immediate feedback, enhancing comprehension and retention of complex topics
Your Mentor
Gain an edge in your career by accessing our team of experts who will provide invaluable guidance and support. Meet our team of mentors!
Data Science Intructors
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Data Science With Python Exam & Certification
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Frequently Asked Questions
Illinois certification validates your proficiency and expertise across various industries. It can enhance your credibility and positively impact your career growth, earning potential, and professional fulfillment. Obtaining Illinois certification opens up opportunities for career advancement and high-paying jobs, bringing ample career opportunities and enhancing your professional success.
Illinois certification is best for IT freshers. This certification program provides comprehensive training and education on various aspects of IT, including programming languages, software development, database management, and more.
Illinois certification helps freshers build a strong foundation in IT. It equips them with the necessary skills to succeed in their careers and to get better job opportunities with higher salaries.
What Our Students Are Saying
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Key Learning Outcomes of Data Science and Machine Learning Course
The key takeaways from the "Entri Elevate Data Science of Machine Learning in Trivandrum" course are as follows
Data Analysis Skills
Proficiency in analyzing and interpreting data using statistical methods and visualization tools.
Machine Learning Algorithms
Understanding and application of supervised and unsupervised learning techniques for predictive modeling and pattern recognition.
Programming Proficiency
Competence in programming languages such as Python or R for data manipulation, analysis, and machine learning implementation.
Data Handling
Knowledge of tools and techniques for processing and analyzing large datasets efficiently.
Data Visualization
Ability to create meaningful visualizations to communicate insights and findings effectively.
Learn About AI Implementation
Develop an AI strategy for your industry and evaluate the various factors involved in its implementation.
Frequently Asked Question about Data Science Course
You will learn data analysis, machine learning algorithms, data visualization, big data handling techniques, and their application in business and industry.
Career paths include data analyst, data scientist, machine learning engineer, business intelligence analyst, and data engineer, among others.
Yes, Trivandrum has a growing demand for data scientists across various industries, including IT, healthcare, finance, and e-commerce, providing ample job opportunities. Students can also explore opportunities outside Kerala as well.
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Advantages of Learning Elevate Data Science of Machine Learning Course
Elevate Learning Experience
Student Support
Available all-day, using Slack for urgent queries.
Q&A Forum
Timely doubt resolution from peers and mentors
Expert Feedback
Personalized feedback on assignments and projects
Industry Networking
Live doubt clearing sessions with Industry Experts
Career Support
Personalized Industry Mentorship
Get personalized feedback and mentorship from an experienced data science specialist to enhance your career path.
Resume Review
Get personalized guidance on your resume structure and content.
Live profile-building workshops
Build your profile with hands-on sessions, whether on your résumé, GitHub, or Kaggle platform.
Upcoming and Recorded Webinars
Who Can Apply For the Course
- Students
- Professionals
- Career Switchers
- Job Seekers
- House Wife
- Un-employers
- Freelancers