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Hands-on, in-person training. 2. Printed training material. 3. Morning Snacks, Lunch, Afternoon Snacks and Refreshments. 4. Guaranteed small class size. The programs are confirmed with a minimum of ten (10) participants and sealed at a maximum of twenty (20) participants. With such small class sizes there will be plenty of time to ask questions and receive personal attention from the faculties. 5. A highly consultative engagement. There will be plenty of time to discuss your specific projects and learning objective to provide immediate return on investments upon completion of any of the program.
Course Overview
In this complex, digital world, clients w ant helps to understand their data to drive greater insight, improved performance and competitiveness.
The course will introduce participants to the important techniques and methods to become more efficient in delivering their daily objectives and also improve their work ethics.
Data & Analytics Academy course for beginners, is designed for: • Graduate Trainees • Data Analysts • Business Analysts • Professionals looking to change career path This course delivers the basic requirement for any aspiring data scientist and big data analysts to make business impact in three days.
The course covers the core concepts of analytics and reporting with introduction to the use of a visualization tool (often Power BI) to entrench the necessary background know ledge.
In this complex, digital world, clients w ant helps to understand their data to drive greater insight, improved performance and competitiveness. The course will introduce participants to the important techniques and methods used by data scientists.
Data & Analytics Academy course for beginners, is designed for:
Course Overview
Becoming a senior data scientist takes more than the understanding of basic skills like statistics and programming in various languages.
The need to develop one area of technical analytic expertise (e.g. machine learning), while being conversant in many others is very critical.
This is the major objective of this course.
Course Overview
Going beyond descriptive analytics has become essential to meet the complexities of information requirement for decision making as well as developing strategies to drive greater profitability, improved performance and competitiveness.
The course builds expertise in advanced analytics, data mining, predictive modelling, quantitative reasoning and web analytics, as well as advanced communication and leadership.
This course integrates data science, information technology and business applications into three areas: data mining, predictive (forecasting) and prescriptive (optimisation and simulation) analytics.
Techniques for building and interpreting mathematical/statistical models of real-world phenomena in and across multiple disciplines, including matrices, linear programming and probability with an emphasis on applications will be covered.
This is for participants who w ant a firm understanding and/or review of these fields of mathematics/statistics prior to applying them in subsequent topics.
Introduction to Statistical Methods
Participants will learn to apply statistical techniques to the processing and interpretation of data from various industries and disciplines.
Topics covered include probability, descriptive statistics, study design and linear regression.
Emphasis w ill be placed on the application of the data across these industries and disciplines and serve as a core thought process through the entire Predictive Analytics curriculum.
Data Preparation
In this course, Participants explore the fundamentals of data management and data preparation.
Participants acquire hands-on experience with various data file formats, working w ith quantitative data and text, relational (SQL) database systems, and NoSQL database systems.
They access, organize, clean, prepare, transform, and explore data, using database shells, query and scripting languages, and analytical software.
This is a case-study- and project-based course with a strong programming component.
Generalised LinearModels
This extends Regression and Multi Analysis by introducing the concept of Generalised Linear Model “GLM”.
Review s the traditional linear regression as a special case of GLM's, and then continues w ith logistic regression, poisson regression, and survivalanalysis.
It is heavily w eighted tow ards practical application w ith large data sets containing missing values and outliers.
It addresses issues of data preparation, model development, model validation, and model deployment.
Intro to Advanced and Predictive Analytics - Regression and Multivariate Analysis
This introduces the concept of advanced and predictive analytics, which combines business strategy, information technology, and statistical modeling methods.
The course review s the benefits of analytics, organisational and implementation/ethical issues.
It develops the foundations of predictive modeling by: introducing the conceptual foundations of regression and multivariate analysis;
developing statistical modeling as a process that includes exploratory data analysis, model identification, and model validation;
and discussing the difference between the uses of statistical models for statistical inference versus predictive modeling.
The high level topics covered in the course include: exploratory data analysis, statistical graphics, linear regression, automated variable selection, principal components analysis, exploratory factor analysis, and clusteranalysis.
In addition Participants w ill be introduced to the R statistical package, and its use in data management and statistical modeling.
The course focuses on the practice of predictive analytics and it is the conclusion of the advanced and predictive analytics program.
It gives participants an opportunity to demonstrate their business strategic thinking, communication, and acquired analytics skills.
Business cases across various industries and application areas illustrate strategic advantages of analytics, as well as organizational issues in implementing systems for predictive analytics.
Participants will work in project teams, generating analytics project implementation plans