Tools and Techniques in Data Science
(Data Mining Data Analytics)
UCERD Gathering Intellectuals Fostering Innovations
Unal Center of Educaiton Research & Development
Unal Center of Educaiton Research & Development
UCERD Rawalpindi
Supercomputing Center
UCERD Murree
Data refers to increasingly larger, more diverse, and more complex data sets that challenge the abilities of traditionally or most commonly used approaches to access, manage, and analyze data effectively. As we are entering an age of Big Data. Big Data give both unique opportunities and amazing challenges. Data science deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. Data science uses complex machine learning algorithms to build predictive models.
Data Scineces and Data Mining are two high-focus of data science. Big Data has become important as many organizations both public and private have been collecting massive amounts of domain-specific information, which can contain useful information about problems such as national intelligence, cyber security, fraud detection, marketing, and medical informatics. Key benefit of Deep Learning is the analysis and learning of massive amounts of unsupervised data, making it a valuable tool for Big Data Analytics where raw data is largely unlabeled and un-categorized. This course provides introduction to big data tools and deep learning frameworks.
Data Scineces and Data Mining are two high-focus of data science. Big Data has become important as many organizations both public and private have been collecting massive amounts of domain-specific information, which can contain useful information about problems such as national intelligence, cyber security, fraud detection, marketing, and medical informatics. Key benefit of Deep Learning is the analysis and learning of massive amounts of unsupervised data, making it a valuable tool for Big Data Analytics where raw data is largely unlabeled and un-categorized. This course provides introduction to big data tools and deep learning frameworks.
1st Week
Past, Present and Future of Data Sciences and Information Technology
2nd Week
Data Sources and Capturing: Data Acquisition, Data Entry, Signal Reception, Data Extraction. This stage involves gathering raw structured and unstructured data.
3rd Week
Maintain: Data Warehousing, Data Cleansing, Data Staging, Data Processing, Data Architecture. This stage covers taking the raw data and putting it in a form that can be used.
4th Week
Mathematical Algorithms Involved in Feature Engineering and Pre Processing
5th Week
Processioning I: Data Mining, Clustering/Classification. Data scientists take the prepared data and examine its patterns, ranges, and biases to determine how useful it will be in predictive analysis.
6th Week
Processioning II: Data Modeling, Data Summarization.
7th Week
Analyze: Exploratory/Confirmatory, Predictive Analysis, Regression, Text Mining, Qualitative Analysis. Here is the real meat of the lifecycle. This stage involves performing the various analyses on the data.
8th Week
Communicate: Data Reporting, Data Visualization, Business Intelligence, Decision Making. In this final step, analysts prepare the analyses in easily readable forms such as charts, graphs, and reports.
9th Week
Mid Exam
10th Week
Different Tool Used for Data Sciences
• Scripting Language (Python)
11th Week
Libraries and Frameworks for Data Sciences
12th Week
Data Science Application Development and Methodologies
13th Week
Big Data Sciences Frameworks
14th Week
Machine and Deep Learning Learning for Data Sciences Problems
15th Week
Revision and Semester Task Evaluation
16th Week
Final Exam
Past, Present and Future of Data Sciences and Information Technology
2nd Week
Data Sources and Capturing: Data Acquisition, Data Entry, Signal Reception, Data Extraction. This stage involves gathering raw structured and unstructured data.
3rd Week
Maintain: Data Warehousing, Data Cleansing, Data Staging, Data Processing, Data Architecture. This stage covers taking the raw data and putting it in a form that can be used.
4th Week
Mathematical Algorithms Involved in Feature Engineering and Pre Processing
5th Week
Processioning I: Data Mining, Clustering/Classification. Data scientists take the prepared data and examine its patterns, ranges, and biases to determine how useful it will be in predictive analysis.
6th Week
Processioning II: Data Modeling, Data Summarization.
7th Week
Analyze: Exploratory/Confirmatory, Predictive Analysis, Regression, Text Mining, Qualitative Analysis. Here is the real meat of the lifecycle. This stage involves performing the various analyses on the data.
8th Week
Communicate: Data Reporting, Data Visualization, Business Intelligence, Decision Making. In this final step, analysts prepare the analyses in easily readable forms such as charts, graphs, and reports.
9th Week
Mid Exam
10th Week
Different Tool Used for Data Sciences
• Scripting Language (Python)
11th Week
Libraries and Frameworks for Data Sciences
12th Week
Data Science Application Development and Methodologies
13th Week
Big Data Sciences Frameworks
14th Week
Machine and Deep Learning Learning for Data Sciences Problems
15th Week
Revision and Semester Task Evaluation
16th Week
Final Exam
Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More
Deep Learning for Medical Image AnalysisVolume:
Author(s):S. Kevin Zhou, Hayit Greenspan and Dinggang Shen (Eds.)
Hands-On Data Analysis with Pandas: A Python data science handbook for data collection, wrangling, analysis, and visualization, 2nd Edition
Python Pandas for Beginners: Pandas Specialization for Data Scientist (Python for Beginners in Data Science and Data Analysis Book 2)
Deep Learning for Medical Image AnalysisVolume:
Author(s):S. Kevin Zhou, Hayit Greenspan and Dinggang Shen (Eds.)
Hands-On Data Analysis with Pandas: A Python data science handbook for data collection, wrangling, analysis, and visualization, 2nd Edition
Python Pandas for Beginners: Pandas Specialization for Data Scientist (Python for Beginners in Data Science and Data Analysis Book 2)
Prof. Tassadaq Hussain Cheema.
He is a permanent faculty member at, Riphah International University.
He did his Ph.D. from Barcelona-tech Spain, in collaboration with Barcelona Supercomputing Center and Microsoft Research Center.
More Details:
He is a member of HiPEAC: European Network on High Performance and Embedded Architecture and Compilation, Barcelona Supercomputing Center and Microsoft ResearchCentre Spain.
Until January 2018, he had more than 19 years of industrial experience including, Barcelona Supercomputing Centre Spain, Infineon technology France, Microsoft Research Cambridge, PLDA Italia, IBM Zurich Switzerland, and REPSOL Spain. He has published more than 80 international publications and filed 5 patents.
Tassadaq's main research lines are Machine Learning, Parallel Programming, Heterogeneous Multi-core Architectures, Single board Computers, Embedded Computer Vision, Runtime Resource Aware Architectures, Software Defined Radio and Supercomputing for Artificial Intelligence and Scientific Computing.
www.tassadaq.ucerd.com
He is a permanent faculty member at, Riphah International University.
He did his Ph.D. from Barcelona-tech Spain, in collaboration with Barcelona Supercomputing Center and Microsoft Research Center.
More Details:
He is a member of HiPEAC: European Network on High Performance and Embedded Architecture and Compilation, Barcelona Supercomputing Center and Microsoft ResearchCentre Spain.
Until January 2018, he had more than 19 years of industrial experience including, Barcelona Supercomputing Centre Spain, Infineon technology France, Microsoft Research Cambridge, PLDA Italia, IBM Zurich Switzerland, and REPSOL Spain. He has published more than 80 international publications and filed 5 patents.
Tassadaq's main research lines are Machine Learning, Parallel Programming, Heterogeneous Multi-core Architectures, Single board Computers, Embedded Computer Vision, Runtime Resource Aware Architectures, Software Defined Radio and Supercomputing for Artificial Intelligence and Scientific Computing.
www.tassadaq.ucerd.com