Data Analysis and Visualization Training, Uyo, Akwa Ibom State.

Data Analysis and Visualization Training and Certification

Course Objectives:

  • Develop proficiency in data visualization tools (Power BI, Tableau)
  • Master data analysis tools (SPSS, Excel, EViews, MiniTab)
  • Learn Python programming and its libraries (NumPy, Pandas, SciPy)
  • Gain expertise in data management tools (SQL, MongoDB, Cassandra)
  • Equip yourself for data jobs like collection, cleaning, analysis, BI, and ML
  • Understand predictive modeling and machine learning algorithms
  • Explore deep learning techniques for unstructured data

Course Outline:

Module 1: Introduction to Data Analytics 

  • What is data analytics?
  • The importance of data analytics in today’s world
  • The different types of data analytics
  • The data analytics lifecycle
  • Applications of data analytics across various industries

Module 2: Data Visualization Essentials 

  • The power of data visualization
  • Choosing the right chart for your data
  • Creating effective data visualizations with Power BI
  • Mastering data visualization techniques in Tableau

Module 3: Data Analysis Fundamentals 

  • Understanding data types and distributions
  • Descriptive statistics and data cleaning
  • Introduction to hypothesis testing and correlation analysis
  • Regression analysis and forecasting techniques

Module 4: Introduction to Python Programming 

  • Python basics: variables, data types, operators, and control flow
  • Functions and modules in Python
  • Object-oriented programming in Python
  • Working with files and databases in Python

Module 5: Python Libraries for Data Analysis 

  • NumPy: efficient numerical computing
  • Pandas: data structures and analysis
  • SciPy: scientific computing and advanced algorithms
  • Matplotlib and Seaborn: data visualization with Python

Module 6: Advanced Data Analysis Techniques 

  • Time series analysis and forecasting
  • Dimensionality reduction and feature engineering
  • Clustering and classification algorithms
  • Machine learning for predictive modeling

Module 7: Introduction to Deep Learning

  • Artificial neural networks and deep learning fundamentals
  • Convolutional neural networks (CNNs) for image recognition
  • Recurrent neural networks (RNNs) for natural language processing
  • Deep learning applications in various fields

Module 8: Data Management and Storage 

  • SQL: querying and manipulating databases
  • MongoDB: NoSQL databases for unstructured data
  • Apache Cassandra: distributed database for high availability

Module 9: Data Collection and Cleaning

  • Techniques for data collection from various sources
  • Data cleaning and preprocessing techniques
  • Data quality management and best practices

Module 10: Business Intelligence and Reporting 

  • Building dashboards and reports with Power BI
  • Data storytelling and communication
  • Business intelligence applications for decision making

Module 11: Career Opportunities in Data Analytics 

  • Identifying potential data analytics jobs
  • Building a strong resume and portfolio
  • Interview preparation and career development tips

Module 12: Hands-on Projects and Case Studies 

  • Real-world data projects using various tools and techniques
  • Applying data analytics to solve business problems
  • Case studies of successful data analytics implementations

Assessment:

  • Daily Class Tasks
  • Weekly Project Presentations
  • Continuous assessment through quizzes and assignments on over 73 Case-Studies
  • Mid-term and final examinations
  • Final Project presentations and portfolio development

Certification:

Upon successful completion of the course, participants will receive a certificate of completion

We also assist our alumni with career guidance and job placement assistance.

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