Big Data + Data Sciences with Machine Learning (Complete Course)
Data Science is a new field that is just shaping up; hence all the confusion around what it really is. It is also a hybrid field that requires multiple skills and training. We felt that professionals thinking of getting into Data Science needed a primer in what this field is all about. Hence, we came up with this course.
This Big Data & Data Sciences course provides you with a practical understanding of big data sciences and emerging technologies like Machine Learning, Natural Language Processing and Data Visualization techniques. You will gain the essential skills and confidence required to apply knowledge and understanding of issues surrounding big data analytics in a range of contexts. There is the opportunity to develop a critical understanding of visualization concepts and emerging technologies, as well as to develop and evaluate new or advanced bespoke solutions for making sense of big and/or complex data (Read more about Big Data).
Course Key Learning [3 Courses]
- BD-01 Machine Learning with Python (Pre-Requisite Course)
- BD-02 Big Data Analytics Introduction
- BD-03 Data Sciences Techniques
- Final Project (Big Data Machine Learning Industry implementation use case)
COURSE-01 Machine Learning Data Analysis with Python
- Basic Concepts, History and Evolution of Machine Learning for Big Data as a business application domain. Use cases in different business industries for both small and big data. A review of Machine Learning techniques and algorithms and their interpretation from a business perspective. Theoretical and Practical Exposure of Cluster Analysis and Support Vector Machines.
KEY Learning
- Python Data Analysis with NumPy and Pandas
- Python Data Visualization – Matplotlib and Pandas
- Math of Machine Learning – Probability, Regression, Vectors, Matrices, Baysian, K Nearest
- Machine Learning with Python – Supervised vs Unsupervised Learning and Train / Test
- Data Mining, Big Data with Machine Leaning – Apache Spark
- Neural Networks and Deep Learning.
- Machine Learning Project – Develop Product Recommender System in Python
COURSE-02 Big Data Analytics
Understand Big Data Background, History and Evolution of Big Data Analytics, Tools, Infrastructures and Technologies. Case studies on how Big Data Analytics has helped solve some of the more concrete real-world problems with enhanced insights into clients’ big data lakes.
Key Learning
- Introduction to Big Data
- Storing Big Data
- Processing Big Data
- Tools and Techniques to Analyse Big Data
- Developing a Big Data Strategy
- Implementing a Big Data Solution
COURSE-03 Data Sciences Techniques
This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data.
KEY Learning
- Introduction and Decision Trees: Typical Data Science Tasks, Data Science, Applications/Examples, Data Preparation, Normalization, Introduction to Classification/Decision Trees, Model Accuracy
- Prediction techniques Continues: Holdout Method, ROC Curve Interpretation, Naïve Bayes, Artificial Neural Network
- Regression and Ensemble Methods: Linear Regression Analysis, Ensemble Methods (Bagging and Boosting), K-Nearest Neighbors Method
- Clustering : K-Means Clustering, Hierarchal Clustering, Fuzzy C-Means
- Text Analysis: Part of Speech tagging, Bags of Words Creation, Part of Speech and Stop Word Filters, Stemming Text, Term Frequency Count
- Market Basket Analysis of Consumer: Association Rule Mining, Apriori Method, Frequent Item sets, Rules Generation, Support, Confidence
Who should attend?
- Senior Executives & Consultant drive key transformation projects for organization
- Industry Professionals willing to develop career in Big Data Sciences
- IT Manager / Business Analyst / Data Analyst/ Data Scientist / Database Admin
Course Pre-Requisites & Credit Hours
- Course Pre-Requisite – None
- Credit Hours – 60 (Lectures 30 hrs + 30 hrs Exercises & Examples )
- Course Duration 3 Months
Educational approach
- Lecture sessions are illustrated with case studies, practical questions and examples
- Practical exercises include Machine Learning, Data Visualization examples and discussions
- Install and Configure you Big Data, Machine Learning platform using industry famous tools
PROGRAM FEE
Admission Fee: 10,000 PKR, Course Fee 60,000 (3 Courses)
Program Total Fees: 70,000 PKR (After Discount, Excluding all applicable taxes)
International Student Fee : 600$| 2,250 AED| 2,250 SAR
Big Data Analyst /Data Scientist / Big Data Professionals Job Market
- Big Jobs – UAE
- Big Data Jobs – Qatar
- Big Data Jobs – Saudi Arabia
- Big Data Jobs – Canada
- Big Data Jobs – Australia
Job Interview Questions
- Data Sciences Job Interview Must Know Questions
- Python Job Interview Questions and Answers
- Data Sciences Job Interview Questions and Answers
- Machine Learning Job Interview Questions
Your FREE eLEARNING Courses (Click Here)
Flexible Class Options
- Evening Classes | Corporate Group Workshops
- Week End Classes For Professionals SAT | SUN
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