Data Science Mentorship Program
The Data Science Mentorship Program is a tailored initiative designed to empower aspiring data scientists and analytics enthusiasts. This program provides a structured learning environment where participants gain hands-on experience and expert guidance to excel in the dynamic field of data science.
View the Course Package to access:
- Tuition details
- Financing options
- Employer sponsorship


Our Facts
300+
Student Enrolled
98%*
Placement Record In 2024
56+
Course Module
Lifetime
Placement Support
Skill Sets for a Successful Data Science Course
Top 11 skills that make you a proficient Data Science Course:
- Programming Proficiency
- Statistical Knowledge
- Data Wrangling
- Machine Learning
- Data Visualization
- Big Data Tools
- Data Storytelling
- Database Management
- Problem-Solving
- Mathematics
- Domain Knowledge
Explore Programs
At Coding Clave Academy, we offer a variety of programs designed to help you learn and grow. Start your journey with us and shape a successful future.
What is Data Science?
Learn how companies use data to make smart business decisions. Our expert trainers in Lucknow teach you how to analyze data, create predictions, and solve real business problems using modern tools and techniques.
Why Choose Data Science?
- High-Demand Career: Growing job opportunities in India and globally.
- Excellent Salary Package: Among the highest-paying tech careers.
- Diverse Applications: Used in healthcare, finance, retail, and more.
- Future-Proof Skills: Always in demand as companies become more data-driven.
- ✓ Learn from industry experts with real project experience
- ✓ Hands-on training with Python, SQL, and ML tools
- ✓ Work on live projects with real company data
- ✓ Interview preparation and resume building
- ✓ Placement assistance with partner companies
- ✓ Access to online learning resources
Data Science Course
Module 1: Foundations of Data Science
Introduction to Data Science
- What is Data Science?: Understanding data-driven decision-making through analytics, statistics, and machine learning.
- Applications in various industries: Leveraging data science in healthcare, finance, marketing, and e-commerce.
- Overview of the Data Science lifecycle: Key steps: data collection, preparation, modeling, evaluation, and deployment.
- Roles and responsibilities of a Data Scientist: Insights from data, predictive modeling, and decision support.
Mathematics for Data Science
- Linear Algebra: Vectors, matrices, and operations:Foundations for machine learning and data transformations.
- Calculus: Derivatives, optimization, and gradients: Core concepts for machine learning algorithms and cost minimization.
- Probability & Statistics: Bayes theorem, distributions: Framework for uncertainty modeling and predictive analytics.
- Mean, variance, and hypothesis testing: Statistical tools for data insights and result validation.
Module 2: Python Programming for Data Science
Python Basics
- Variables, data types, and operators: Core building blocks for data storage and computations in Python.
- Loops, conditionals, and comprehensions: Efficient iteration, decision-making, and compact code structures.
- Functions, modules, and error handling: Modular programming, reusable code, and managing runtime errors.
Python Libraries
- NumPy: Arrays, indexing, slicing, matrix operations: Numerical computations with fast, multidimensional array manipulation.
- Pandas: Dataframes, merging datasets, groupby operations: Handling structured data for analysis and transformations.
- Matplotlib & Seaborn: Creating static and interactive visualizations: Visual tools for data exploration and insights.
Module 3: Data Wrangling and Preprocessing
Data Cleaning Techniques:
- Identifying and handling missing data: Strategies like imputation or removing incomplete records.
- Removing duplicates and dealing with outliers:Ensuring data integrity and addressing extreme values.
- Data standardization and normalization:Preparing data for consistency and improving model performance.
Feature Engineering:
- Encoding categorical data (label encoding, one-hot encoding):Converting categories into numerical format for machine learning.
- Feature scaling (min-max scaling, standardization): Adjusting feature values for uniform model input.
- Feature selection and dimensionality reduction: Identifying important features and reducing dataset complexity.
Module 4: Data Visualization
Exploratory Data Analysis (EDA)
- Identifying trends, patterns, and anomalies in datasets: Uncovering insights for better decision-making through visual exploration.
- Correlation matrices and pair plots:Understanding relationships between variables using graphical representations.
Visualization Tools
- Advanced plots with Seaborn: Heatmaps, violin plots, and pair grids:Creating detailed and meaningful visualizations for data insights.
- Dashboard creation using Tableau or Power BI: Building interactive dashboards for presenting data-driven stories.
Module 5: Machine Learning Essentials
Supervised Learning
- Linear Regression: Simple and multiple regression models:Predicting continuous outcomes with single or multiple predictors.
- Logistic Regression: Classification problems and evaluation:Solving binary and multi-class classification tasks effectively.
- Decision Trees and Random Forest:Tree-based algorithms for both regression and classification tasks.
- Evaluation metrics: Accuracy, precision, recall, F1 score: Assessing model performance with essential evaluation metrics.
Unsupervised Learning
- Clustering algorithms: K-means and Hierarchical clustering:Grouping data points into clusters based on similarities.
- Principal Component Analysis (PCA) for dimensionality reduction:Reducing features while preserving essential data variance.
Module 6: Advanced Machine Learning
Neural Networks and Deep Learning
- Introduction to perceptrons and multi-layer neural networks: Understanding the basics of neural networks and layered architectures.
- Activation functions: ReLU, Sigmoid, Tanh:Functions that introduce non-linearity to neural networks.
- Optimization techniques: Gradient Descent and Backpropagation: Core methods for training and refining neural network models.
Time Series Analysis
- Forecasting techniques: ARIMA, SARIMA models:Predicting future trends based on time-dependent data.
- Seasonal decomposition and smoothing:Breaking down time series data into components for analysis.
Module 7: Big Data and Cloud Computing
Big Data Ecosystem
- Introduction to Hadoop and Spark:Tools for scalable storage and distributed data processing.
- Data pipelines and distributed data processing:Automating workflows for handling large-scale data.
Cloud Platforms
- Data handling in AWS, Azure, and Google Cloud:Managing and analyzing data on leading cloud platforms.
- Setting up virtual machines and data storage: Configuring cloud infrastructure for computation and storage needs.
Module 8: Projects and Case Studies
End-to-End Projects
- Real-time projects using datasets like Kaggle or UCI Machine Learning Repository: Hands-on experience with diverse, real-world datasets.
- Build predictive models and deployment pipelines:Creating actionable insights and implementing end-to-end solutions.
Industry Applications
- Predictive analysis in retail or healthcare:Leveraging data to forecast trends and improve decision-making.
- Sentiment analysis for marketing or product reviews:Understanding customer opinions to refine strategies and enhance services.
Module 9: Tools and Technologies
- Programming Tools:Python, R, and SQL.
- Version Control: Git and GitHub.
- Integrated Development Environments (IDEs):Jupyter Notebook, PyCharm.
- Data Platforms:Tableau, Power BI, or Google Data Studio.
Hands-On Practices
- Working with Kaggle datasets for challenges like Titanic survival prediction.
- Sentiment analysis on Twitter datasets.
- Forecasting sales data for e-commerce.
- Image classification with deep learning models.
Frequently Asked Questions
- A basic understanding of mathematics and statistics .
- Social Media Marketing (SMM)
- Familiarity with programming languages like Python or R ( though some courses start from scratch ) .
- A keen interest in data analysis and problem solving .
- Proficiency in Python , R, and SQL .
- Knowledge of machine learning algorithms and data visualization tools like Tableau and Power BI .
- Hands on experience with real-world datasets through projects .
- Data Scientist
- Data Analyst
- Machine Learning Engineer
- Business Intelligence Specialist The demand for skilled professionals in these fields is rapidly growing in Lucknow and beyond .