Professional Course in Data Science
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. If students complete all four levels NCC Education, UK also provide an additional certificate.
Topics covered for Semester:1 (Total Duration: 160 Hours)
| Module 1: An Introduction (Duration: 80 Hours) | ||||
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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.
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| Module 2: Intermediate (Duration:80 Hours) | ||||
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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.
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Topics covered for Semester:2 (Total Duration: 240 Hours)
| Module 3: Advanced (Duration:120 Hours) | ||||
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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 â summarsing information, results and findings for management, drawing conclusions translating to business decisions.
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| 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.
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WHAT YOU WILL LEARN
- Data Science Tools & Technologies
- Life Cycle of a Data Science Projects
- Statistics for Data Science
- Python for Data Science
- Exploratory Data Analysis
- Advanced Statistics & Predictive Modeling
- Get introduced to Machine Learning
- Big data and Data mining
- Time Series and forecasting
- Use R to clean, analyze, and visualize data
- Navigate the entire data science pipeline from data acquisition to publication
- Use GitHub to manage data science projects
Enroll By- Daffodil International Academy and NCC education (UK) Jointly.
Requirement â Minimum HSC or Diploma.
All Our Programs Include
- International Certificates
- Real-world projects from industry experts
- Technical mentor support
- Personal career coach and career services
- Flexible learning program
- Student community
- Interview preparations
- Resume services
- Github review
- LinkedIn profile review
Why should I enroll?
The data science field is expected to continue growing rapidly over the next several years, and there is a huge demand for data scientists across industries. It is consistently rated as a top career. DIA has collaborated with industry leaders to offer a world-class learning experience so you can advance your data science career. You will get hands-on experience running data pipelines, designing experiments, building recommendation systems, and more. Youâll have personalized support as you master in-demand skills that will qualify you for high-value jobs in the data science field. By the end of this program, you will have an impressive portfolio of real-world projects and valuable hands-on experience. You will also receive career support via profile and portfolio reviews to ensure you are ready to establish a successful data science career and land a job you love.
What jobs will this program prepare me for?
Obtaining the skills required to be a Data Scientist will make you extremely valuable across many industries and in many roles. Data Scientists work as Analysts, Statisticians, Engineers, and more. Some become Data and Analytics Managers, while others specialize as Database Administrators. As a graduate of this program, youâll be prepared to seek out roles that run the gamut from generalist to specialist, and all points in between.
How do I know if this program is right for me?
This program offers an ideal path for experienced programmers and data analysts to advance their data science careers. If you are interested in deepening your expertise in the fields of analytics, machine learning, data engineering, and/or data science, this is a great way to get hands-on practice with a variety of techniques and learn to build end-to-end data science solutions.