Data Science Overview Turkmenistan Eğitimi

Who Should Attend?

• Basic Understanding of Data Concepts: Participants should have a basic understanding of data and its importance in business contexts, including familiarity with common data types and simple data manipulation.
• Fundamental Analytical Skills: A basic proficiency in analytical thinking and problem-solving, enabling participants to follow along with data analysis concepts and techniques presented during the course.
• Familiarity with Business Operations: An understanding of general business operations and processes, as this will help in relating data science concepts to practical business applications and decision-making scenarios.

This course combines engaging instructor-led presentations and useful demonstrations and group discussions. Throughout the course you will explore:

• The essentials of data collection and cleansing, recognizing their role as the foundation for insightful analytics.
• The dynamics of data visualization, and how well-crafted visuals aid in decision-making and idea communication.
• A high-level overview of machine learning, exploring its ability to identify patterns and forecast trends for informed business strategies.
• The fundamentals of big data technologies, understanding their capacity to manage and analyze extensive information for business leverage.
• The significance of data-driven insights in steering strategic, informed actions and nurturing a data-aware business culture.
• The framework of data teams, appreciating the collaborative synergy among data scientists, analysts, and engineers in driving data initiatives.
• Key programming languages and tools used in data science, acknowledging their role in effective project execution and team communication.

If your team requires different topics, additional skills or a custom approach, our team will collaborate with you to adjust the course to focus on your specific learning objectives and goals.

1. Understanding Data Science in Business
   
   • Overview of Data Science and its Evolution
   • Importance of Data Science in Business
   • Key Terminologies in Data Science
   • Data Science vs. Traditional Business Intelligence
   • Demo
2. Data Collection and Preparation
   
   • Learn about the sources of data and methods to collect and prepare data for analysis.
   • Sources of Data in Business
   • Data Collection Techniques
   • Data Cleaning and Pre-processing
   • Demo
3. Introduction to Analytics and Statistical Methods
   
   • Explore basics of analytics and statistical methods used in data science.
   • Descriptive vs. Inferential Statistics
   • Common Statistical Methods and Their Business Applications
   • Basics of Data Visualization
   • Demo
4. Introduction to Machine Learning for Business
   
   • Understand the fundamentals of machine learning and its application in business decisions.
   • Overview of Machine Learning
   • Supervised vs. Unsupervised Learning
   • Common Machine Learning Algorithms in Business
   • Demo
5. Data Visualization and Interpretation
   
   • Learn how to visualize and interpret data to derive actionable business insights.
   • Importance of Data Visualization
   • Common Data Visualization Tools (Tableau, PowerBI, Qlik, Looker etc.)
   • Demo
6. Big Data, Hadoop, and Business Decisions
   
   • Understand the concept of big data, the Hadoop ecosystem, and how it can be leveraged for strategic business decisions.
   • Introduction to Big Data
   • Overview of the Hadoop Ecosystem and its Components
   • Big Data Technologies and Tools (Hadoop, Spark, Hive, Cassandra, etc.)
   • Integrating Big Data into Business Strategy
   • Big Data for Predictive Analysis
   • Challenges and Ethical Considerations in Big Data
   • Demo
7. Common Tools, Languages & AI in Data Science
   
   • Gain insights into the common tools and languages used in data science, the role of AI, and modern data science roles.
   • Common Data Science Tools and Languages (Python, R, SQL, and Java)
   • Introduction to AI in Data Science
   • New AI Tools in Data Science
   • Modern Roles in Data Science (Data Analyst, Data Engineer, Data Scientist)
   • Demo
8. Implementing Data Science in Business
   
   • Gain insights on how to successfully implement data science projects in a business environment.
   • Building a Data-Driven Culture
   • Roles and Responsibilities in a Data Science Team
   • Project Management for Data Science
9. Data Science in Action
   
   • Q&A session & Recap
   • Group discussion on the application of data science in participants' respective businesses
   • Additional resources and guides for further self-paced learning