Professional Course in Data Science
Course Duration: 400 Hours.
With the growing flow of information (Big Data), the need for managing information in a structured way has grown immensely. Data Science has proven itself to be the way to manage all this information in a professional manner. Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems for extracting, analyzing, visualizing, managing, and storing data to create business insights. These insights help companies make powerful data-driven decisions which ultimately creates the highest paying job opportunities across many interdisciplinary fields in the present world. Daffodil has come forward to provide you with the opportunity to master the skillsets required for the hottest professions of the decade. This course will help anyone interested in pursuing a career in data science or machine learning to develop career-relevant knowledge and expertise. This course is certificated by NCC Education, UK.
Topics covered for Semester:1 (Total Duration: 160 Hours)
|Module 1: An Introduction (Duration: 80 Hours)|
1.Statistics: Understand how to solve problems with algorithms, use summary and inferential statistics to inform business decisions.
2. Coding: Develop and Testing Programme Code, basic Python programming and be able to create a database using SQL.
3. Modelling: Introduction to modelling (types of models), data preparation for modelling, model evaluation and selection.
4. Data Visualisation: Basic storytelling and design principles for data visualisation, dashboard design for communication and selecting the right charts and graphs and understanding Data visualisation.
|Module 2: Intermediate (Duration:80 Hours)|
1.Statistics: : Understand probability theory & random variables, concepts of summary statistics and be able to perform linear regression analysis.
2. Coding: Intermediate level of Python programming, undertake data analysis, familiarise with Python data structures and create visualisation packages for graphics.
3. Modelling: Introduction to model lifecycle management, predictive analytics, supervised and unsupervised learning, statistical data modelling techniques, time series modelling and applications and demonstrate understanding for data preparation for modelling.
4. Data Visualisation: Design principles for data visualisation, Exploratory, descriptive & diagnostic analysis, Data story-telling and Dashboard design for communication.
Topics covered for Semester:2 (Total Duration: 240 Hours)
|Module 3: Advanced (Duration:120 Hours)|
1.Statistics: : Understand basic properties of matrix and vectors: scalar multiplication, linear transformation, transpose, conjugate, rank, determinant. Familiarize with inner and outer products, matrix multiplication rule and various algorithms, matrix inverse.
2. Coding: Undertake advanced data analysis with R and Selecting and using with R data structures with confidence.
3. Modelling: Using multiple regression models, Advanced model lifecycle management, Analytics scenarios for different industries, Decision Trees and Model evaluation & deployment.
4. Data Visualisation: Management level advanced story telling – summarsing information, results and findings for management, drawing conclusions translating to business decisions.
|Module 4: Expert (Duration:120 Hours)|
1.Statistics: : Understand the basics of optimisation and how to formulate the problem and be able to formulate the linear programming and the integer programming.
2. Coding: Evaluate Big Data concepts, Data collection using web scraping tool and Understand the importance of data mash-ups infrastructure for storing, processing and analysing big data.
3. Modelling: Advanced model lifecycle management.
4. Data Visualisation: Board level reporting, drawing inferences, strategic business decisions and conclusions.
|Semester||Tuition fee||50% Scholarship on tuition fees||Registration fee|
|Semester 1||Tk. 30,000||Tk. 15,000||£ 80|
|Semester 2||Tk. 40,000||Tk. 20,000||£ 100|
|Admission fee||Tk. 5000 + 5000||00||00|
|Total||Tk. 80,000||Tk. 35,000||£ 180|
|Total payable:||Tk.35,000 + £180|