Data science is an interdisciplinary academic field that uses statistics, scientific computing, algorithms, and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data.
Accredited By:
An ISO 9001:2015 &
Teacher Scientist Network Certified
Course Overview:
Welcome to the Professional Certification Program in Data Science and Data Analytics! This comprehensive 180-day course is designed to equip aspiring data scientists and data analysts with the skills and knowledge necessary to thrive in the fast-paced world of data-driven decision-making. With over 20 years of industry and teaching experience, our expert instructors will guide you through a well-structured curriculum, enabling you to develop a deep understanding of data science concepts and practical data analytics techniques.
Course Objectives:
– Gain proficiency in data manipulation, exploration, and visualization using popular tools and libraries such as Python, R, and SQL.
– Master statistical concepts and their applications in making data-driven decisions.
– Learn the fundamentals of machine learning and apply algorithms for predictive modeling and pattern recognition.
– Understand big data technologies and how to work with large datasets efficiently.
– Develop skills in data storytelling and communication to present insights effectively to stakeholders.
– Work on real-world projects and case studies to apply your knowledge and build a professional portfolio.
Course Structure:
The 180-day program is divided into nine distinct modules, each focusing on essential topics in data science and data analytics:
– Overview of data science and its applications
– Data-driven decision making
– Introduction to data analysis tools and libraries (e.g., Python, R, SQL)
– Data cleaning and handling missing values
– Data transformation and feature engineering
– Data integration and data reduction techniques
– Exploratory data analysis (EDA)
– Data visualization libraries (e.g., Matplotlib, Seaborn, ggplot2)
– Communicating insights through visualizations
– Probability and statistical distributions
– Hypothesis testing and confidence intervals
– Regression analysis and model evaluation
– Supervised learning algorithms (e.g., Linear Regression, Decision Trees, Random Forests, SVM)
– Unsupervised learning algorithms (e.g., K-means, PCA)
– Model evaluation and hyperparameter tuning
– Introduction to big data and distributed computing frameworks (e.g., Hadoop, Spark)
– Handling large-scale datasets
– Introduction to NLP and its applications
– Text preprocessing and feature extraction
– NLP models and sentiment analysis
– Introduction to neural networks
– Deep learning frameworks (e.g., TensorFlow, PyTorch)
– Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
– Ethical considerations in data science
– Data privacy and security
Projects:
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1. Exploratory Data Analysis (EDA)
Analyzing and visualizing a dataset to draw meaningful insights and patterns.
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2. Predictive Modeling
Building a predictive model using machine learning algorithms for a real-world dataset.
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3. Data Visualization Dashboard
Creating an interactive data visualization dashboard to showcase insights.
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4. Text Analytics and Sentiment Analysis
Applying NLP techniques to analyze sentiment in customer reviews or social media data.
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5. Image Recognition
Building a deep learning model for image recognition tasks.
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6. Big Data Analysis
Analyzing large-scale datasets using distributed computing frameworks.
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7. Data Ethics Analysis
Exploring ethical considerations in a data science project and proposing solutions.
Tools & Technologies:
Course Essentials:
Who Should Enroll :
Prerequisites:
Invest in Your Future:
Key Highlights
Who Should Enroll :
This program is ideal for professionals and students with a passion for data analysis and a desire to work with large datasets to drive business decisions. Whether you are an aspiring data scientist, data analyst, business analyst, or someone seeking to transition into the data science field, this course will provide you with the necessary skills and confidence to excel in your career.
Prerequisites:
While prior programming experience is not mandatory, a basic understanding of Python or R will be beneficial. Familiarity with statistics and mathematics at the undergraduate level is recommended but not required.
Invest in Your Future:
Don’t miss this opportunity to advance your career in data science and data analytics. Enroll in our Professional Certification Program, and let our seasoned instructors guide you through a transformative learning journey that will empower you to harness the power of data effectively.
Key Highlights
120 Hrs Live Sessions (5+1 Month Projects Session)
250 Hrs of Applied Learning
160 Hrs of Self-paced Learning
Weekday/Weekend Lectures Available
Placement Assistance
9+ Industry Projects & Case Studies
2 Capstone Projects
Dedicated Learning Management Team
1:1 Mock Interview
24*7 Support
Learn from Industry Expert
ISO & TSN affiliated Certification
Designed for Working Professionals and Freshers
No-cost EMI
Earned with Excellence:
Your Certificate
of Achievement
Your Certificate
of Achievement
