Certificate Course in
Data Analytics For Managers
- 48 Hours Classroom & Online Sessions
- 80+ Hours Assignments & eLearning
- 100% Job Assistance
- 2 Capstone Projects
- Industry Placement Training
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"The Data Analytics course has been designed for Managers, Senior Executors, and Decision Makers with an aptitude for analyzing the business data to take advantage while making appropriate decisions. The course details the aspects of Analytics like Statistical Analysis, Explainable Machine Learning Algorithms, the black box model Neural Network, and its architectures. Finally, the program enlightens and encourages the adoption of Analytics for Data-Driven Decision Making.
Data Analytics
Total Duration
3 Months
Prerequisites
- Computer Skills
- Basic Mathematical Concepts
- Analytical Mindset
Data Analytics For Managers Course Overview
This certificate program on Data Analytics Course provides an overview of how Python and R programming can be employed in Data Mining of structured (RDBMS) and unstructured (big data) data. Comprehend the concepts of Data Preparation, Data Cleansing, and Exploratory Data Analysis. Perform Text Mining to enable Customer Sentiment Analysis. Learn Machine learning and developing Machine Learning Algorithms for predictive modeling using Regression Analysis. Assimilate various black-box techniques like Neural Networks, SVM, and present your findings with attractive data visualization techniques. By Data Analytics course in Malaysia, students will be empowered with skills in Data Analytics, Data Mining, Machine Learning, Predictive Modelling, and Regression Analysis in addition to programming languages Python, R and Tableau.
What is Data Analytics?
Data Analytics is the process of delivering valuable insights from data through quantitative and qualitative approaches. The process consists of extracting data and categorising it into various forms, to make it resourceful and valuable. At present many Data Analytics techniques use software combined with Machine Learning algorithms, Artificial Intelligence, and other specific features.
The various types of Data Analytics are Prescriptive Analytics, Predictive Analytics, Diagnostic Analytics, and Descriptive Analytics. Every organisation is getting morphed to a data-driven industry. It has to depend on categorising and analysing data for making productive decisions. Globally in many companies, Data Analytics is used to deliver operational efficiencies and unprecedented opportunities.
A Data Analyst has to explore or fetch data from various sources and design it to maintain resourceful databases and data systems. Data visualization, Data mining, Data cleaning are part of the roles of Data Analyst. A skilled Data Analyst should have statistical and domain knowledge. There are other roles of Data Analyst in various fields such as Marketing Analyst, Operation Analyst, Financial Analyst, Digital Analyst, etc. There is substantial demand for Data Analysts but there is a lack of supply. There is a huge requirement of skilled Data Analysts and people who love analytics can undoubtedly opt for this career. Apart from the lucrative income, there are excellent perks and a lot of liberties. In every sector Data Analysts are necessary to analyze data, they are not limited to specific terrain.
Data Analytics For Managers Course Outcomes in Malaysia
Who Should Attend
Block Your Time
Who Should Sign Up?
- IT Engineers
- Data and Analytics Manager
- Business Analysts
- Data Engineers
- Banking and Finance Analysts
- Marketing Managers
- Supply Chain Professionals
- HR Managers
- Math, Science and Commerce Graduates
Data Analytics For Managers Course Modules in Malaysia
By Data Analytics course in Malaysia, students will be exposed to the application of Data Analytics tools in various projects. Understand the finer concepts of predictive analysis and descriptive analysis. Students will be able to perform Data cleansing, categorization of data to devise better strategies. As part of this module, students will get adequate training on Python, R languages, and its applications to solve business problems. Evaluation techniques are explained by calculating the measure of Error (RMSE). Regression Models like Collinearity, Heteroscedasticity, Overfitting, and Underfitting will be explained. As part of this module, the difference between a Time series data and Cross-sectional data is explained. Forecasting techniques explain the response variables variations based on time. Get introduced to the time series components, and the various visualization techniques to interpret the components. Understand the different types of Forecasting techniques available to churn the data. And many other important concepts are explained in the Data Analysis course in Malaysia. Students will be exposed to Black box techniques, Neural Network processing, and Data visualization process.
- Introduction to Big Data
- Data, Data, Data everywhere
- Data and its uses – a case study (Grocery store)
- Interactive marketing using data & IoT – A case study
- Stages of Analytics
- Descriptive Analytics
- Diagnostic Analytics
- Predictive Analytics
- Prescriptive Analytics
- Machine Learning Categories
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Data Science Project Lifecycle
- Frameworks for Building Machine Learning Systems
- Knowledge Discovery Databases (KDD)
- SEMMA (Sample, Explore, Modify, Model, Assess)
- Cross-Industry Standard Process for Data Mining
- KDD vs. CRISP-DM vs. SEMMA
- CRISP-DM
- Business Understanding
- Define Business Problem – Objective and Constraints
- Assess and Analyze Scenarios
- Define Data Mining Problem
- Project Plan
- Data Understanding
- Data Collection
- Data Description
- Exploratory Data Analysis
- Data Quality Analysis
- Data Preparation
- Data Integration
- Data Wrangling
- Feature Extraction and Engineering
- Attribute Generation and Selection
- Modeling
- Selecting Modeling Methods
- Model Training
- Model Evaluation and Improving by Tuning
- Model Assessment
- Evaluation
- Data Partition
- Evaluate on Training Data
- Evaluate on Validation Data
- Evaluate on Test Data
- Deployment
- Business Understanding
- Data Collection
- Primary Sources
- Surveys
- Simulations
- Sensors Data
- Design of Experiments, etc
- Secondary Sources
- Data Warehouses
- Data Lakes
- Databases (SQL, NoSQL, etc.)
- Data and Datasets
- Structured Data vs. Unstructured Data
- Big Data vs. Regular Size Data
- Cross-Sectional Data vs. Time Series Data
- Balanced vs. Imbalanced Data
- Offline vs. Real-Time Data
- Population and Sample
- Sampling Techniques
- Probability Sampling (Unbiased)
- Non-Probability Sampling (Biased)
- Sampling Techniques for Handling Balanced vs. Imbalanced Datasets
- Random Resampling - Under & Over Sampling
- K-fold Cross-Validation
- SMOTE - Synthetic Minority Oversampling Technique
- MSMOTE - Modified SMOTE
- Cluster-Based Sampling
- Inferential Statistics
- Sampling Variation
- Central Limit Theorem
- Confidence Interval - Concept
- Confidence Interval with Sigma
- t-Distribution/Student's-t Distribution
- Confidence Interval without Sigma
- Population Parameter Standard Deviation Known
- Population Parameter Standard Deviation Not Known
- Sampling Techniques
- Various Graphical Techniques to Understand Data
- Univariate
- Line Charts
- Bar Plots
- Dot Charts
- Histograms/Frequency Distribution
- Box Plots/Box and Whisker Plots
- Density Plots
- Q-Q Plots/Normal Quantile – Quantile Plots
- Bivariate
- Scatter Plots
- Univariate
- Business Understanding
- Formulating a Hypothesis Statements
- (Ho) Null Hypothesis – Default Condition/Current Condition/Status Quo
- (Ha/H1) Alternative Hypothesis – Action Condition
- Type I – (Alpha) – Caused by Rejection of a True Ho
- Type II Errors – Caused by Not Rejecting a False Ho
- Hypothesis Test Cases
- 1 Sample z-test
- ANOVA
- 2 Proportion Tests
- Introduction to Predictive Analytics
- Correlation and Causation
- Measure of Correlation
- Model Evaluation
- Decision Tree – Pros and Cons
- Introduction to Neural Network
- Introduction to Deep Learning Techniques
- Data Mining Process
- Supervised vs Unsupervised Learning
- Introduction to Hierarchical Clustering / Agglomerative Clustering
- Introduction Non-Hierarchical Clustering / K-Means Clustering
- Association Rules - Market Basket / Affinity Analysis / Relationship Mining
- Recommendation Engine
- Introduction to Time Series Data
- Model-Based approaches
- Smoothing Techniques
- Moving Average
- Exponential Smoothing
- Holts / Double Exponential Smoothing
- Winters / Holt-Winters
- AutoML Methods
- AutoML Systems
- AutoML on Cloud
Data Analytics For Managers Trends in Malaysia
Data Analytics Course in Malaysia has gathered steam and will evolve in the years to come. The advent of Big Data has led to a data explosion of sorts. Big Data Analytics has opened myriad opportunities for students and working professionals. The Data Analytics Course includes an introduction to foundation Data analytics as well as Advanced Data Analytics using Python and R programming. Students who complete this course can command very high salaries in Malaysia and other countries. Python which is the top programming tool and R which is second best allows you to accomplish a lot of routine data analytics tasks with ease.
Malaysia with its peninsular mainland in the South - Eastern part of Asia is rich in Flora & Fauna. It is one of the fastest-growing economies and has embraced emerging technologies from around the globe. Many industries in the federal capital region of Kuala Lumpur are now using advanced Data Analytics for their business operations. Petronas, the leading company in Kuala Lumpur has been using Data Analytics for quite some time. Analytics professionals with knowledge for Python and R are now very much in demand because businesses are obsessed with Data Analytics is driven decision making.
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Additional Assignments of over 60-80 hours
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