Diploma in Big Data Analytics

Introduction


The convergence of big data and machine learning with technologies such as cloud services, sensors, ubiquitous computing, mobile devices and the Internet of Things has created vast new opportunities for business. Analytics has become a competitive and sustainable advantage for many organizations. To harness the benefits of big data and machine learning, however, business leaders face the pressing challenge of not only acquiring the right technologies and talent to analyze and interpret the data, but also to weave a data-centric mindset into the organization's structure and cultural fabric.

This Four-Month Diploma will empower with the skills and confidence to tackle data-driven opportunities and accelerate data-analysis transformation in the organization. Through lectures, case studies and discussions, real-world insights will be gain on various applications of big data analytics and machine learning, and how they can be used to fuel better decision-making within the context of attendee's own Department/Organization.

Number of Credits

144 Credits

Format

On-campus

Tuition

Rs. 1382/ per credit hour

Enrollment

Full-time

Duration

4 Months

Registration

Open

Course Outlines


All Courses in BDA Diploma are Hands-on Courses Particularly Focused on Python and R language

Semester 1

Course 1: Python Foundations for Big Data Analytics
Setting the Foundations: Setting up Python and Jupyter Notebook and Python Development Environments, History and Evolution of Python, Python Language Basics, Data and Control Structures, and Functions with Practice Examples

Numerical Data Analysis: Array Fundamentals, Vectorized Computations, Array-Oriented Programming Concepts, How Python is Simpler than other Languages, Simplified Numerical Analysis Concepts related to Machine Learning with practice examples

Course 2: Big Data Wrangling: Getting Exploratory Insights into your Big Data Lakes
Setting the Perspective: Background, History and Landscape Evolution of Big Data Analytics, Tools, Infrastructures and Technologies. BDA Case studies that solved concrete real-world industrial problems with enhanced insights into clients' big data lakes.

Getting Down to Business: Exploratory Data Analysis (Dimensional Analytics, Statistical Analysis, Descriptive Statistics), Data Cleaning and Preparation (Data Transformations, Feature Selection, Missing Value Analysis), The Role of Meta Data in Data Lake Management

Course 3: Business Intelligence (BI) and Big Data Visualization
Unleash the power of Data Visualization in Business Analytics of Big Data: Relationship between Big Data and BI, Basic BI evolution, BI evolution from small to big data use cases. BI history, Basic BI concepts for dashboarding, "Which type of chart to select for which kind of analyses with which kind of data?", Practical Exposure to State-of-the-Art BI tools for Big Data.

Semester 2

Course 1: Big Data Management Systems with NoSQL Data Stores
NoSQL Databases: Hallmark data silos for big data management and storage. Theoretical foundations and Practical Exposure of MongoDB (Document Store), Redis (Key-Value Store), Cassandra (Columnar Store), and Neo4J (Graph Store)

Case Studies: "How NoSQL databases are Solving Major Big Data Management Problems of Different Industries?".

Course 2: Machine Learning for Big Data
Machine Learning Basics: Basic Concepts of Classification, Regression and Cluster Analysis, Evolution of Machine Learning Algorithms, Use cases in Business Industries for Small and Big Data. Operationalizing Machine Learning Models: Architecture and Strategy

Getting Down to Business: Theoretical foundations and Practical Hands-on with State-of-the-Art Machine Learning Algorithms, e.g., Logistic Regression, Ensemble Methods, Neural Networks, Naïve Bayes, Nearest Neighbor, Simple and Multiple Linear Regression, K-Means and Agglomerative/Hierarchical Customer Segmentation

Course 3: Infrastructure Development for Real-Time Big Data Analytics
Standard technologies and infrastructures for Big Data Analytics in real-time. Lambda and Kappa Architecture, Apache Spark's Streaming, Apache Storm and Apache Kafka. Use cases of these solutions in doing real-time big data analytics and the derived business value. Recommendations (derived from personal experience and industrial trend) for developing an in-house infrastructure for real-time big data analytics.

Digital marketing course aims at providing participants with a well-defined set of digital skills which can be utilized by client-side marketing teams, digital and integrated agencies. The program will help in driving strategy and tactical solutions for enterprise e-commerce businesses, B2B & B2C businesses, communications and public relations agencies, businesses managed by owners, and web marketing organisations.

  • Fundamentals of marketing and with understanding of changing trends and the rise of the digitalization in marketplace
  • Effective engagement with customers via social media platforms
  • Basics of e-Commerce including: the ideal structure of an eCommerce site, impact of site design on customer transactions
  • Development of the online marketing strategy across the digital space
  • Understanding & Implementation of a search engine strategy
  • Creation and management of a data strategy
  • Identification of effective and ineffective strategies for determining the marketing spend
  • In-depth grounding in Digital Advertising

On completion of this course, learners will be able to:

  • Understand the impact of technology on the traditional marketing mix
  • Understand how digital marketing can be used to increase sales and to grow businesses
  • Understand the fundamentals of the digital marketing
  • Understand the elements of the digital marketing plan
  • Understand how to get to online target market and develop basic digital marketing objectives

Target Audience


Leaders and senior officials interested in building analytics capabilities to drive change within their Departments/Organization.

Candidates preferably having a bachelor's degree are eligible to apply.

Program Fees


Program Fees: PKR 199,000/- (Exclusive of SST) (excluding all applicable taxes)

15% discount is offered if full fee is paid in advance

Advance Payment Schedule Total
Total fee in Advance PKR 169,000
Installments Payment Schedule Total
Total Fee (if paid in 4 equal installments) PKR 49,750 PKR 49,750 PKR 49,750 PKR 49,750 PKR 199,000

Fee Includes:

Processing fees, Test and Exam Fees, Diploma and Transcript. Fees do not include course material, books and stationery.

Admission fees PKR 1000/-

Payment

Payment is due upon receipt of the acceptance of participants to the program along with the invoice.

Please ensure that the payment reaches the CICT Office before commencement of the program because seat in the class will only be reserved once the fee is received.

Payment can be made via cheque / bank draft payable to the "Institute of Business Administration, Karachi" at the following address:

Center for Information & Communication Technology (CICT)
IBA, City Campus, Garden/Kayani Shaheed Road, Karachi.

For online payments via credit cards: https://onlinepayment.iba.edu.pk/

Trainer's Profile


Dr. Tariq Mahmood

Dr. Tariq Mahmood
Dr. Tariq Mahmood is Professor at the Institute of Business Administration (IBA), Karachi. He also heads the Big Data Analytics Laboratory (BDA-LAB) at IBA with a primary research focus on discovering optimized infrastructures for integrating and analyzing Big Data/OSINT variety. He has 10 years of professional and research experience in the domains of Business Intelligence, Predictive Analytics, Data Warehousing, Data Science, Big Data, and Advanced Analytics. He also has 8 years of professional and consultancy experience in these domains and focuses on spreading the use of these domains in Pakistani corporate sector. He is a regular speaker, participant and panelist at local events targeting Data Science, Predictive Analytics and Big Data. He has conducted numerous training and workshops on Big Data, both for government and private organizations.


Dr. Muhammad Affan

Dr. Muhammad Affan
Dr. Muhammad Affan Alim has 16 years of teaching, research, and development experience in Machine Learning, Deep Learning, Data Science, pattern classification, computer vision, Optimization of models, and statistical & mathematical analysis. He also has several years of professional experience in software development in Pakistan and the United Kingdom (UK). He has developed several industry-based projects.




Mr. Sohail Imran

Mr. Sohail Imran
For more than 18 years, Mr. Sohail Imran is conducting training and workshops for databases (SQL and NoSQL), Big Data Infrastructure, and Machine Learning to different institutes, universities, and the corporate sector.

More than 8 years of professional experience in Big Data Analytics, Data Science, Data Mining, Data Warehousing and DBMS (SQL and NoSQL). Providing consultancy in designing and developing Big Data Analytics platform using Java, RapideMiner, Radoop, Python, Hadoop, Hive, Spark, Kafka, Spark Streaming, Storm, etc.


Mr. Muhammad Rizwan

Mr. Muhammad Rizwan
Muhammad Rizwan provides digital leadership to organizations, from strategy to execution, globally and locally. In his 25+ corporate career, he has worked in both public and private equity spaces with technology-led and digitally-enabled businesses in various management vernaculars, including C-Suite.

His experience includes software offerings from transaction recording to ERP. He has been an official trainer for Oracle ERP (Ebusiness Suite) at Oracle University. More recently, involved in providing businesses actionable intelligence.

He holds a certificate from Stanford University in Machine Learning. A master's degree from Hamdard. Bachelor's degree in Statistics/Commerce and Information Systems. He also carries an international diploma in Software Engineering.

Currently, heading the information systems at Dollar Industries (Pvt) Ltd. He has worked for Hino Pak Motors, Karachi Stock Exchange, CPLC (Car theft software) projects.

Duration


  • 4 months including 6 courses
  • Saturday & Sunday