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Professional Certification Program in Data Science & Data Analytics.

  • Last Update August 12, 2023
  • 0 already enrolled


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


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Course Essentials:

Earned with Excellence:
Your Certificate
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  • Course level: Expert
  • Total Enrolled 0
  • Last Update August 12, 2023
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