DATADRIX : Data Science

Navigating the World of Data Science

Harnessing the power of data to gain valuable insights and make informed decisions. Combining statistical analysis, machine learning, and domain expertise to extract meaning from complex datasets. Driving innovation & solving complex problems through data-driven approaches & predictive modeling. By combining techniques from statistics, computer science & domain expertise, data scientists uncover hidden patterns & trends within vast datasets. These revelations drive informed decision-making, improve processes, and unlock innovation in a wide range of fields, from business and healthcare to technology and beyond. With the ability to turn data into actionable intelligence.

Course Curriculum

A syllabus is a meticulously crafted document that serves as a comprehensive roadmap for the training program. It plays a pivotal role in guiding candidate along their learning journey, offering a structured framework for acquiring knowledge and honing skills.

Module 1

  • Introduction to Python Programming
  • Variable & Datatypes
  • Conditional & Looping Statements
  • Functions & File Handling
  • Exception Handling & Threading
  • Searching & Sorting
  • Object Oriented Programming
  • Python Libraries
  • Handling structural Datasets
  • Data Manipulation
  • Data Cleaning
  • Importing data from mutiple sources
  • Finding Insights from datasets

In our Python course, we delve into the powerful ecosystem of Python libraries that enhance the functionality and efficiency of your coding projects. Students will explore widely-used libraries such as NumPy for numerical computations, Pandas for data manipulation and analysis, and Matplotlib and Seaborn for data visualization. Additionally, the course covers libraries like Scikit-Learn for machine learning, BeautifulSoup for web scraping, and Flask for web development. Each library is introduced with practical examples and hands-on exercises, enabling students to understand their applications and integrate them into their projects. By mastering these libraries, participants will be equipped with the tools needed to tackle complex programming challenges and develop sophisticated applications, making them valuable assets in the field of software development.

  • Introduction to SQL and MySQL
  • Data Creation and Retrieval
  • Data Filtering
  • Data Analysis using aggregate functions and group by
  • Joins and Keys
  • MySQL Joins
  • Subqueries and Views
  • Window/Analytical Functions
  • Case Study

Module 2

  • What is Data Visualization?
  • Data Visualization in Python
  • Matplotlib and Seaborn
  • Line Charts & Bar Graphs
  • Histograms, Scatter Plots & Heat Maps

Data cleaning is a crucial part of our Python course, focusing on the essential techniques required to prepare raw data for analysis. This section of the course teaches students how to handle missing values, detect and correct errors, and ensure consistency in datasets using powerful Python libraries such as Pandas and NumPy. Students will learn to identify outliers, standardize data formats, and manage duplicate records, gaining hands-on experience with real-world datasets. The curriculum emphasizes practical skills through projects and exercises, enabling students to transform messy data into clean, reliable datasets ready for analysis. By mastering data cleaning, students will be well-equipped to tackle data-driven challenges and contribute effectively to data science and analytics projects.

  • Introduction to Data Cleaning and Data Types
  • Exploring and Visualization the missing values
  • Advanced-Data Cleaning Concepts
  • Introduction to Feature Engineering
  • Feature Extraction and Transformation
  • Feature Selection and Dimensionality Reduction
  • Introduction to Statistics
  • Probability Theory
  • Statistical Inference I
  • Statistical Inference II
  • Regression Analysis I
  • Regression Analysis II
  • Supervised vs Unsupervised Machine Learning
  • Regression vs Classification
  • Modeling with Linear Regression
  • Training & Testing data
  • Model Evaluation

Module 3

  • Underfitting & Overfitting
  • Model Selection
  • Model Tuning
  • Cross validation
  • Confusion Matrix , Recall , Precision , F1 Score
  • AUC and ROC Curves
  • Model Intution
  • Case Studies

Our Machine Learning course delves into the core concepts of regression and classification, equipping students with the knowledge and skills to tackle a wide range of predictive modeling problems. In the regression module, learners will explore linear and polynomial regression, delve into techniques such as ridge and lasso regression, and understand how to evaluate model performance using metrics like RMSE and R². The classification module will cover essential algorithms like logistic regression, decision trees, support vector machines, and ensemble methods such as random forests and gradient boosting. Students will gain hands-on experience with real-world datasets, learning to preprocess data, select appropriate features, and fine-tune models for optimal performance. By the end of the course, participants will be proficient in building and evaluating robust regression and classification models, ready to apply these techniques in various domains such as finance, healthcare, and marketing.

  • Introduction to Decision Trees
  • Implementation of Decision Trees
  • Introduction to the Concept of Bagging
  • Introduction to Concept of Random Forest
  • Introduction to Boosting
  • Introduction to extreme Gradient Boosting
  • Introduction to Imbalanced Machine Learning models
  • Introduction to Recommendation Engines
  • Overview of Natural Language Processing and Text Mining
  • ImplText Mining, Cleaning and Pre-processing
  • Vectorization , TF-IDF
  • Bag of Words Model
  • Text Classifier & its deployement

Module 4

Dimensionality reduction is a crucial concept in our machine learning course, aimed at enhancing your ability to handle high-dimensional data efficiently. This module delves into techniques such as Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Linear Discriminant Analysis (LDA). You will learn how to reduce the number of features in a dataset while preserving essential information, which helps improve model performance and reduce computational costs. Practical exercises will illustrate how to apply these techniques to real-world datasets, enabling better visualization, faster training times, and mitigating the curse of dimensionality. By mastering dimensionality reduction, you will gain valuable skills to preprocess data effectively and build more robust machine learning models.

In our Python course, we emphasize the importance of version control and collaboration through the use of Git and GitHub. Students will learn the fundamentals of Git, including how to initialize repositories, track changes, and manage branches. We’ll explore how GitHub serves as a platform for collaborative coding, enabling students to contribute to open-source projects, collaborate on team assignments, and manage their own repositories. Practical exercises will include creating pull requests, resolving merge conflicts, and utilizing GitHub Actions for continuous integration. By integrating Git and GitHub into the curriculum, we ensure that students not only develop strong Python programming skills but also gain essential experience in modern software development practices.

In our Python course, assignments are crafted to reinforce the concepts and techniques covered in each module, ensuring a deep and practical understanding of Python programming. These assignments range from basic exercises, such as writing functions and working with data structures, to more complex tasks like implementing algorithms, handling exceptions, and performing file I/O operations. Students will also engage in mini-projects that involve real-world applications, such as data analysis, web development, and automation. Each assignment is designed to challenge students’ critical thinking and problem-solving skills, while providing ample opportunities for creativity and exploration. By completing these assignments, students will build a strong foundation in Python and gain the confidence to tackle advanced programming challenges.

In our Python course, project deployment is a key focus, equipping students with the skills needed to take their applications from development to a live environment. Throughout the course, participants will learn how to deploy Python projects using various tools and platforms such as Flask, Django, Docker, and cloud services like AWS and Heroku. The curriculum covers essential deployment concepts, including setting up virtual environments, managing dependencies, configuring servers, and optimizing applications for performance and security. By the end of the course, students will not only have built impressive Python projects but will also have the knowledge and confidence to deploy their applications efficiently, ensuring they are accessible and functional in real-world scenarios. This comprehensive approach to deployment prepares students for the practical challenges they will face in the tech industry, making them versatile and job-ready Python developers.

Internship Program

This internship is a part of the course curriculum to help you gain real experience in the Data Science domain.During this internship, you will go through various challenges which you allow to explore new skills and push your limits while learning something new during the projects.

Topics Covered :

Integration of python & SQL

Web Scrapping

Data Cleaning with Python

Model Evaluation

Git / Github Integration

End to End Projects

Interview Preparation

Datadrix offers top-notch placement opportunities. With strong industry ties and modern training, we excel in placing our candidates. Our results speak to our commitment to shaping successful careers. Our approach ensures to open pathway for learners to achieve good growth in the domain

Activities Covered :

Interview Pattern Preparation

Mock Interview Practice Sessions

Preparation as per Job Description

Placement Ready Session for Working Professionals

Technical Screening for technical strengthening

Screening for effective communication check

Placement

Datadrix offers top-notch placement opportunities. With strong industry ties and modern training, we excel in placing our candidates. Our results speak to our commitment to shaping successful careers. Our approach ensures to open pathway for learners to achieve good growth in the domain

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Duration

Our 150+ hour data science course offers in-depth training and hands-on experience, covering everything from data collection to advanced analysis and visualization, preparing you to excel in the data-driven world.

Assignments

Our data science course includes assignments that offer hands-on training and cover data collection, analysis, and visualization, equipping you with essential skills for real-world professional success.

Projects

Our data science course features projects that offer practical, hands-on training in data collection, analysis, and visualization, equipping you with essential skills and real-world experience for success.

Live Classes

Our data science course includes live classes offering hands-on training and real-time guidance. From data collection to advanced analysis and visualization, you’ll gain essential skills to excel in today’s tech-driven world.

Classnotes

Our data science course includes detailed class notes that cover data collection, analysis, and visualization, providing the essential skills and practical knowledge needed to excel in the tech-driven world.

Interview Preparation

Our data science course includes targeted interview preparation, covering data collection, analysis, and visualization. This training equips you with the essential skills needed to excel in interviews and succeed in the tech-driven world.

Placements

Our data science course offers dedicated placement support, focusing on data collection, analysis, and visualization. This training equips you with the skills needed to succeed in the tech-driven world and secure your ideal job.

An Awesome Community

Our students, instructors and mentors come from different colleges, companies, and walks of life.

Meet our team & students

Joining DATADRIX means you’ll create an amazing network, make new connections, and Leverage Diverse Opportunities

“Validate Your Expertise and Propel Your Career”

  • Expand Opportunities: Certifications to unlock new career opportunities, gain credibility with employers, and open doors to higher-level positions.
  • Continuous Growth: Certifications not only validate your current skills but also encourage continuous learning and professional development, allowing you to stay updated with the latest industry trends and advancements.
  • Certification: A testament to your skills and knowledge, certifications demonstrate your proficiency in specific areas of expertise, giving you a competitive edge in the job market.
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Data Science

Building Tomorrow’s Solutions from Today’s Data

A data science course is a structured educational program designed to teach individuals the knowledge, skills, and tools necessary to work as a data scientist. The course typically covers a wide range of topics, including statistics, machine learning, data analysis, data visualization, programming languages (such as Python or R), and data manipulation techniques. Through hands-on projects and practical exercises, participants gain experience in working with real-world datasets and applying data science methodologies to solve complex problems. The goal of a data science course is to equip individuals with the necessary skills to collect, analyze, and interpret large volumes of data to drive informed decision-making and derive actionable insights.

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Deepak Chahal
Devansh Goel
Ishty Malhotra
Taneesha Agrawal
Mayank Rana
Shubham Naithani
Kartik Sharma
Upvansh Thakur
Naveen Negi
Amita Dwivedi
Abhinav Bishnoi
Himanshu Pal
Jay Verma
Neha Sharma
Rishabh Upadhyay

Key Features & Benefits

  • Practice problems of varying difficulty
  • Over more than 5000+ Questions
  • 1:1 Expert Doubt support
  • Mock interviews with career guidance
  • 12 + Years of exprienced Faculty
  • Deep Explaination of coding
  • Practical & Project Based Learning
  • Structured feedback to make you better
  • Resume Profile Building
  • Offline / Online Modes
  • Interview Preparation
  • Production Workflow
  • Secure Certification
  • Git Github Integration
  • 24/7 Support Team
  • Projects from the scratch

Frequently Asked Questions

Prerequisites for Data Science

Knowledge of data manipulation, analysis, and visualization tools is beneficial for a comprehensive understanding of the field.

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What is ETL Process in data science

Raw Data extracted, transformed into a structured format, and then loaded into a target database for further analysis

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Career in Data Science Course in india

With high demand and diverse applications, professionals play a pivotal role in shaping the future of technology and business.

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Does Data Science have future

Increasing demand for skilled data scientists reflects its sustained importance in shaping the future of technology and business.

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