Professional Certificate Data Science
February 18, 2025 5:35 pmProfessional Certificate in Data Science
Online Course
Professional Certificate in Data Science
The Professional Certificate in Data Science is designed for individuals eager to dive into the world of data and gain practical skills in extracting meaningful insights from large datasets. Throughout this 9-month course, students will learn essential techniques such as data analysis, machine learning, deep learning, natural language processing, and big data technologies. The program covers Python, data wrangling, visualization, and statistical methods, offering hands-on experience with real-world datasets and case studies.
Data Science is one of the most sought-after fields, with applications across diverse industries such as finance, healthcare, retail, technology, and government. Organizations are constantly looking for data-driven insights to make informed decisions, improve operations, and innovate. As a result, the demand for skilled data scientists, machine learning engineers, and business analysts has skyrocketed.
Graduates of this program will be equipped with the skills necessary for roles such as Data Scientist, Machine Learning Engineer, Data Analyst, Business Intelligence Specialist, and AI Developer. Opportunities exist in various sectors including healthcare (predictive analytics), retail (customer segmentation), finance (fraud detection), and technology (AI development). With the growth of Big Data, Cloud Computing, and Artificial Intelligence, data science professionals are expected to have significant career opportunities, making it one of the most promising and well-compensated fields in the global job market.
Course Fee
₹ 50,000/-
Qualification
Any Degree
Duration
6 Months
Course Type
Certification
Our Recognitions Speaks
Creative Mentors was honored for its excellency in animation education industry
WHAT WE TEACH
Here is a 9-Month Course Curriculum Module-wise for Professional Certificate in Data Science. This comprehensive program covers everything from fundamental concepts to advanced techniques, focusing on key skills required in data science, including data analysis, machine learning, statistics, and programming.
Month 1: Introduction to Data Science and Programming Basics
- Topics Covered:
- What is Data Science? Overview of the Data Science Process
- Key Roles in Data Science: Data Scientist, Data Analyst, and Data Engineer
- Introduction to Data Science Tools: Jupyter Notebook, Google Colab, Anaconda
- Setting Up Python Environment for Data Science
- Introduction to Version Control (Git, GitHub)
- Practical Assignment: Set up Python and tools for data science; create your first Python script in Jupyter.
- Topics Covered:
- Introduction to Python Programming
- Data Types, Variables, and Functions
- Conditional Statements and Loops
- Lists, Tuples, and Dictionaries
- File Handling and Working with Data Files (CSV, Excel)
- Topics Covered:
Month 2: Data Wrangling and Visualization
- Topics Covered:
- Introduction to Pandas Library
- DataFrames, Series, and Indexing
- Data Cleaning: Handling Missing Data, Duplicates, and Formatting
- Merging, Joining, and Concatenating DataFrames
- Data Aggregation and Grouping
- Practical Assignment: Clean a raw dataset and manipulate it using Pandas functions.
- Topics Covered:
- Introduction to Data Visualization
- Plotting with Matplotlib: Line Plots, Histograms, Bar Plots
- Advanced Visualizations with Seaborn: Heatmaps, Pairplots, Box Plots
- Customizing Plots and Saving Visualizations
- Topics Covered:
Month 3: Statistical Analysis for Data Science
- Topics Covered:
- Descriptive Statistics: Mean, Median, Mode, Standard Deviation
- Probability Theory and Distributions (Normal, Binomial, Poisson)
- Inferential Statistics: Hypothesis Testing, P-Values, Confidence Intervals
- Correlation and Causation
- Topics Covered:
- Topics Covered:
- Sampling Techniques and Central Limit Theorem
- Confidence Intervals and Hypothesis Testing
- ANOVA, Chi-Square Test, and T-Test
- Regression Analysis and its Applications
- Practical Assignment: Conduct hypothesis testing on sample data and apply statistical methods for decision-making.
- Topics Covered:
Month 4: Introduction to Machine Learning
- Topics Covered:
- What is Machine Learning? Types of Machine Learning (Supervised, Unsupervised)
- Overview of Algorithms: Linear Regression, Decision Trees, K-Nearest Neighbors
- Splitting Data into Training and Testing Sets
- Model Evaluation: Accuracy, Precision, Recall, F1 Score
- Practical Assignment: Build a simple linear regression model and evaluate its performance.
- Topics Covered:
- Topics Covered:
- Supervised Learning Algorithms: Linear Regression, Logistic Regression
- Overfitting and Underfitting
- Cross-Validation Techniques
- Implementing Models using Scikit-Learn
- Practical Assignment: Apply supervised learning algorithms to classify data and predict outcomes.
- Topics Covered:
Month 5: Advanced Machine Learning Techniques
- Topics Covered:
- Clustering Algorithms: K-Means, Hierarchical Clustering, DBSCAN
- Dimensionality Reduction: PCA (Principal Component Analysis)
- Anomaly Detection
- Association Rule Mining (Apriori, FP-Growth)
- Practical Assignment: Implement K-Means clustering and PCA on real datasets.
- Topics Covered:
- Topics Covered:
- Model Tuning: Grid Search, Random Search
- Feature Engineering and Feature Selection
- Ensemble Methods: Random Forest, Gradient Boosting
- Model Deployment Basics
- Practical Assignment: Tune machine learning models and optimize performance.
- Topics Covered:
Month 6: Deep Learning and Neural Networks
- Topics Covered:
- Introduction to Neural Networks
- Architecture of Neural Networks: Perceptron, Activation Functions
- Training Neural Networks: Backpropagation, Gradient Descent
- Introduction to TensorFlow and Keras
- Practical Assignment: Build a basic neural network using Keras for image classification.
- Topics Covered:
- Topics Covered:
- Convolutional Layers and Pooling Layers
- Image Classification with CNNs
- Transfer Learning
- Implementing CNN using TensorFlow/Keras
- Practical Assignment: Build a CNN for image recognition (e.g., MNIST dataset).
- Topics Covered:
Month 7: Natural Language Processing (NLP)
- Topics Covered:
- Text Preprocessing: Tokenization, Lemmatization, Stemming
- Feature Extraction: Bag of Words, TF-IDF
- Word Embeddings (Word2Vec, GloVe)
- Introduction to NLP with Python Libraries (NLTK, spaCy)
- Practical Assignment: Preprocess and extract features from text data for classification.
- Topics Covered:
- Topics Covered:
- Sentiment Analysis Techniques
- Text Classification with Machine Learning Models
- Word Embeddings for Sentiment Analysis
- Building NLP Models with Scikit-Learn and TensorFlow
- Practical Assignment: Implement sentiment analysis on a dataset of customer reviews.
- Topics Covered:
Month 8: Data Science in Real-World Applications
- Topics Covered:
- Data Science for Business Decision Making
- Predictive Analytics in Business: Sales Forecasting, Customer Segmentation
- Healthcare Data Analysis: Patient Outcome Predictions, Disease Modeling
- Practical Assignment: Analyze business or healthcare datasets and create predictive models.
- Topics Covered:
- Topics Covered:
- Introduction to Big Data Concepts
- Data Storage and Processing: Hadoop, Spark
- Cloud Computing: AWS, Google Cloud, Azure for Data Science
- Working with Distributed Systems
- Practical Assignment: Use cloud platforms to process big data and train machine learning models.
- Topics Covered:
Month 9: Capstone Project and Industry Applications
- Topics Covered:
- Data Collection and Cleaning
- Building Machine Learning and Deep Learning Models
- Model Evaluation and Deployment
- Presentation of Findings and Insights
- Practical Assignment: Complete a final project applying all learned concepts, showcasing your data science skills from problem formulation to model deployment.
- Topics Covered:
By the end of the 9-month program, students will have comprehensive knowledge of data science, including programming, data manipulation, machine learning, deep learning, NLP, and big data techniques. They will have hands-on experience in building and deploying models, enabling them to pursue roles like Data Scientist, Machine Learning Engineer, Business Intelligence Analyst, and AI Specialist in various industries.
TO START AN EXCITING CREATIVE CAREER
OUR FACULTY
FROM THE STUDENTS
Direct testimonials from the students who completed the course
ADMISSION PROCESS
Creative Mentors Animation, Gaming and VFX School is looking for dedicated students who want to become tomorrow’s art and design leaders. We seek innovators, storytellers, collaborators, problem solvers, dreamers, leaders—all are welcome here.
