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.
An ISO 9001:2015 &
Teacher Scientist Network Certified
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.
– 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.
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
1. Exploratory Data Analysis (EDA)
Analyzing and visualizing a dataset to draw meaningful insights and patterns.
2. Predictive Modeling
Building a predictive model using machine learning algorithms for a real-world dataset.
3. Data Visualization Dashboard
Creating an interactive data visualization dashboard to showcase insights.
4. Text Analytics and Sentiment Analysis
Applying NLP techniques to analyze sentiment in customer reviews or social media data.
5. Image Recognition
Building a deep learning model for image recognition tasks.
6. Big Data Analysis
Analyzing large-scale datasets using distributed computing frameworks.
7. Data Ethics Analysis
Exploring ethical considerations in a data science project and proposing solutions.
Tools & Technologies:
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.
Earned with Excellence: