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2024

Data Mining & Business Intelligence

Code: IT

Duration: 5 Days

Location: Doha

Fee: QAR 12,900

Course Overview

In this 5-day intensive course on Data Mining & Business Intelligence, participants will start with the essentials of statistical concepts, moving swiftly into advanced analytical techniques like hypothesis testing and regression. Day three emphasizes mastering tools such as Excel and Power BI, followed by a deep dive into data collection and cleaning methodologies on day four. The course culminates with hands-on sessions on data exploration, visualization, and advanced techniques in data wrangling and feature engineering, equipping learners to transform data into meaningful insights.

Course Objectives

By the end of the course, participants will be able to:
  • Understand the foundational statistical concepts crucial for data analysis.
  • Master advanced statistical techniques, including hypothesis testing and regression analysis.
  • Gain proficiency in data tools like Excel, Power BI, Tableau, and QlikView.
  • Acquire skills to source, collect, and ensure the quality of data from various platforms.
  • Develop expertise in exploratory data analysis to identify patterns and summarize data.
  • Enhance data preparation capabilities through advanced data wrangling and feature engineering techniques.

Who Should Attend?

Business analysts, aspiring data scientists, and managers keen on leveraging data for strategic insights should attend this course. It's also ideal for IT professionals transitioning into data-centric roles, marketing experts aiming to harness data for pinpointed campaigns, and students or graduates from fields like statistics and business. Essentially, any professional eager to amplify their decision-making capabilities through data-driven insights would benefit immensely from this course.

Course Contents

Module (01) Foundations of Data Analysis
  • Introduction to statistical concepts: mean, median, mode.
  • Importance of variance and standard deviation.
  • The role of probability in decision-making.
  • Data types and their significance.
  • Case studies: Real-world applications of basic statistics.
Module (02) Advanced Statistical Techniques
  • Dive into hypothesis testing: p-values and significance.
  • Exploring correlation: Understanding relationships between variables.
  • Basics of regression analysis.
  • Predictive vs. descriptive statistics.
  • Workshop: Hands-on statistical analysis using real datasets.
Module (03) Tools for Data Analysis
  • Mastery of Excel: Formulas, charts, and pivot tables.
  • Introduction to Power BI: Connecting data sources and visualization.
  • Overview of Tableau, QlikView, Python, and R.
  • Choosing the right tool for the job.
  • Practice session: Data visualization challenges.
Module (04) Ensuring Data Quality
  • Techniques for effective data collection.
  • Cleaning data: Handling missing values and outliers.
  • Tools and strategies for data preprocessing.
  • The importance of consistent data for analysis.
  • Activity: Cleaning a messy dataset.
Module (05) Data Transformation and Insights
  • Exploratory Data Analysis (EDA): Techniques and importance.
  • Data wrangling: From raw data to analysis-ready.
  • Introduction to feature engineering.
  • Strategies for effective data transformation.
  • Group project: Transforming and analyzing a provided dataset.

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