Training
"Our training programs are designed to equip individuals and organizations with the necessary skills and knowledge to effectively harness the power of data."
Introduction to Data Analytics
Course Description: This course is an introduction to data analytics, focusing on foundational concepts, tools, and techniques used to analyze and visualize data. Students will learn to use popular data analysis software and programming languages to explore and analyze real-world data sets. Topics covered will include data cleaning and preparation, data visualization, exploratory data analysis, and basic statistical techniques.
- Understand the basic principles of data analytics and its applications in various fields
- Identify and acquire relevant data for analysis
- Clean, transform and prepare data for analysis
- Use popular data analysis tools and programming languages
- Create and interpret basic visualizations of data
- Conduct exploratory data analysis using statistical techniques
Course Outline:
| Definition and importance of data analytics |
| Types of data analytics: descriptive, predictive, and prescriptive |
| Overview of data analytics tools and technologies |
| Sources of data: internal and external |
| Data cleaning and transformation |
| Data preparation for analysis |
| Importance of data visualization |
| Types of data visualization: graphs, charts, and maps |
| Tools and techniques for creating visualizations |
| Overview of statistical concepts |
| Measures of central tendency and dispersion |
| Correlation and regression analysis |
| Overview of predictive modeling |
| Linear regression and logistic regression |
| Decision trees and random forests |
| Overview of prescriptive modeling |
| Optimization techniques: linear programming and integer programming |
| Monte Carlo simulation |
| Case studies of data analytics in various fields |
| Ethical and social implications of data analytics |
| Future trends in data analytics |
| Homework assignments (40%) |
| Midterm exam (20%) |
| Final project (40%) |
| Recommended Textbook: Data Analytics Made Accessible by Anil Maheshwari |
| Prerequisites: No prior knowledge of data analytics is required. Basic knowledge of statistics and programming concepts would be helpful. |
Building Data Analytics Capabilities
Course Description: This course is designed to help participants develop data literacy skills that are essential for making informed decisions in a data-driven world. Participants will learn how to understand and interpret data, identify data sources, and communicate insights effectively. The course will cover topics such as data visualization, statistical concepts, and data ethics.
Learning Objectives: By the end of this course, participants will be able to:
- Understand the basics of data and its applications
- Analyze and interpret data effectively
- Use data visualization tools to communicate insights
- Identify data sources and evaluate their quality
- Apply basic statistical concepts to understand data
- Understand ethical considerations around data collection and use
Course Outline:
| Overview of the course |
| The role of data literacy in decision-making |
| Key concepts and terminology |
| Understanding data sources and formats |
| Data exploration and descriptive statistics |
| Data analysis tools and techniques |
| Introduction to data visualization |
| Choosing the right chart types |
| Best practices for creating effective data visualizations |
| Identifying data sources |
| Evaluating data quality |
| Managing data effectively |
| Introduction to statistical concepts |
| Probability and distributions |
| Hypothesis testing and confidence intervals |
| Ethical considerations in data collection and use |
| Privacy and data protection laws |
| Responsible data use and sharing |
| Weekly assignments (70%) |
| Final project (30%) |
| Recommended Textbook: Data Literacy: What It Is, Why You Need It, and How to Get It by David Benzel |
Prerequisites: No prior knowledge of data or statistics is required. Basic knowledge of business concepts would be helpful. Basic knowledge of Excel or any other spreadsheet program would be beneficial. |
Generating Value from Data
Course Description: This course is designed to help organizations generate business value from data. Participants will learn how to identify business problems that can be solved with data analytics, design and implement data analytics solutions, and communicate insights effectively. The course will cover topics such as data preparation, data visualization, machine learning, and big data technologies.
Learning Objectives:
By the end of this course, participants will be able to:
- Identify business problems that can be solved with data analytics
- Design and implement data analytics solutions
- Prepare data for analysis
- Apply machine learning techniques to solve business problems
- Communicate insights effectively through data visualization
- Evaluate the effectiveness of data analytics solutions
- Understand big data technologies and their impact on data analytics
Course Outline:
| Overview of the course |
| The role of data analytics in generating business value |
| Key concepts and terminology |
| Understanding business problems |
| Identifying business problems that can be solved with data analytics |
| Defining the problem statement and objectives |
| Overview of the data analytics process |
| Data preparation and data cleaning |
| Exploratory data analysis |
| Overview of machine learning |
| Supervised and unsupervised learning |
| Decision trees, clustering, and regression |
| Overview of data visualization |
| Choosing the right chart types |
| Best practices for creating effective data visualizations |
| Overview of data analytics evaluation |
| Choosing evaluation metrics |
| Interpreting evaluation results |
| Overview of big data technologies |
| Hadoop, Spark, and NoSQL databases |
| The impact of big data on data analytics |
| Weekly assignments (70%) |
| Final project (30%) |
| Recommended Textbook: Data Science for Business by Foster Provost and Tom Fawcett |
Prerequisites: No prior knowledge of data analytics is required. Basic knowledge of business concepts would be helpful. Basic knowledge of statistics and programming would be beneficial. |
Building Data Literacy
Course Description: This course is designed to help participants develop data literacy skills that are essential for making informed decisions in a data-driven world. Participants will learn how to understand and interpret data, identify data sources, and communicate insights effectively. The course will cover topics such as data visualization, statistical concepts, and data ethics.
Learning Objectives: By the end of this course, participants will be able to:
- Understand the basics of data and its applications
- Analyze and interpret data effectively
- Use data visualization tools to communicate insights
- Identify data sources and evaluate their quality
- Apply basic statistical concepts to understand data
- Understand ethical considerations around data collection and use
Course Outline:
| Overview of the course |
| The role of data literacy in decision-making |
| Key concepts and terminology |
| Understanding data sources and formats |
| Data exploration and descriptive statistics |
| Data analysis tools and techniques |
| Introduction to data visualization |
| Choosing the right chart types |
| Best practices for creating effective data visualizations |
| Identifying data sources |
| Evaluating data quality |
| Managing data effectively |
| Introduction to statistical concepts |
| Probability and distributions |
| Hypothesis testing and confidence intervals |
| Ethical considerations in data collection and use |
| Privacy and data protection laws |
| Responsible data use and sharing |
| Weekly assignments (70%) |
| Final project (30%) |
| Recommended Textbook: Data Literacy: What It Is, Why You Need It, and How to Get It by David Benzel |
Prerequisites: No prior knowledge of data or statistics is required. Basic knowledge of business concepts would be helpful. Basic knowledge of Excel or any other spreadsheet program would be beneficial. |
Data Analytics Tools and Techniques
Course Description: This course is designed to provide an overview of the key tools and techniques used in data analytics. Participants will learn how to extract, clean, transform, and analyze data using popular tools such as Excel, Python, and R. The course will cover topics such as data preprocessing, data visualization, statistical analysis, and machine learning.
Learning Objectives:
By the end of this course, participants will be able to:
- Use Excel, Python, and R to extract and analyze data
- Apply data preprocessing techniques to clean and transform data
- Create visualizations to communicate insights
- Use statistical techniques to analyze data
- Apply machine learning techniques to solve predictive problems
Course Outline:
| Overview of the course |
| Introduction to data analytics tools and techniques |
| Understanding data types and formats |
| Data extraction and manipulation using Excel |
| Data cleaning techniques |
| Data transformation and reshaping |
| Introduction to data visualization tools |
| Best practices for creating effective visualizations |
| Advanced visualization techniques |
| Introduction to statistical concepts |
| Inferential statistics and hypothesis testing |
| Regression analysis and forecasting |
| Introduction to machine learning |
| Supervised and unsupervised learning |
| Model selection and evaluation |
Weekly assignments (70%)
Final project (30%)
Recommended Textbook: Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by Wes McKinney
Prerequisites: Basic knowledge of statistics and programming concepts would be helpful. Basic knowledge of Excel or any other spreadsheet program would be beneficial. No prior experience with Python or R is required, but familiarity with programming concepts would be beneficial.
| Overview of big data technologies |
| Hadoop, Spark, and NoSQL databases |
| The impact of big data on data analytics |
| Weekly assignments (70%) |
| Final project (30%) |
| Recommended Textbook: Data Science for Business by Foster Provost and Tom Fawcett |
Prerequisites: No prior knowledge of data analytics is required. Basic knowledge of business concepts would be helpful. Basic knowledge of statistics and programming would be beneficial. |
Introduction to Data Analytics
Building Data Analytics Capabilities
Generating Value from Data
Data literacy refers to the ability to read, interpret, and communicate data effectively. It encompasses a range of skills, including the ability to collect, analyze, and visualize data, as well as the ability to draw insights and make decisions based on data. Data literacy is becoming increasingly important as organizations of all types and sizes collect and use data to drive decision-making. In order to be truly data-driven, organizations need employees at all levels who are comfortable with data and can use it to drive results. Data literacy is therefore an important skill for professionals across a wide range of industries, including finance, healthcare, marketing, and more.
V-TEKI
"Strategically enhancing prowess in data analytics, driving value realization, and fostering data fluency to empower clients in unleashing their data's full potential and achieving substantial business impact."
- +62-812 8788 8009
- +62-21 29 490 608
- Info@v-teki.com
- www.v-teki.com