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Exploratory Data Analysis

  • Training Duration:
  • 3 Days
  • Training Environment:
  • On-site at the Institution
  • In DVA Classrooms
  • Online
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About the Training

This is a training program for professionals who want to learn the fundamental analytical methods in data science. Today, data plays a critical role in every aspect of the business world. When analyzed correctly, it creates significant value in companies’ strategic decision-making processes. This training program aims to help participants gain a deep understanding of data analysis processes. It is designed to equip them with the skills to effectively explore complex datasets and extract meaningful insights.

The training covers topics such as laying a foundation for data analysis, examining, cleaning, processing, and analyzing data. Participants will gain knowledge about various data visualization techniques and tools. They will learn how to present data in an understandable and impactful way. Additionally, topics such as statistical analysis methods, data mining techniques, and the basics of machine learning are included in the program.

This training program comprehensively addresses the essential tools and techniques for making data-driven decisions in the business world. Participants will solidify their theoretical knowledge through hands-on work with real-world datasets. They will enhance their practical skills. During the training, participants will learn the fundamental principles of data analysis and discover ways to overcome challenges they may encounter in data science projects.

The “Exploratory Data Analysis Training” is ideal for individuals working or aiming to work in data-centric roles such as data analyst, data scientist, business analyst, market researcher, and others. This training gives participants a data-driven perspective, enabling them to contribute to data-based decision-making processes in the business world. The program also enhances participants’ analytical thinking and problem-solving skills, helping them become more effective and proficient in the world of data science and analytics.

In conclusion,

The “Exploratory Data Analysis Training” is a valuable resource for professionals interested in data science and analytics and those aiming to pursue a career in this field. The training not only provides participants with a strong foundation in data analysis but also equips them with the necessary tools and skills to be successful in data-driven decision-making processes in the business world. This program helps participants gain a competitive advantage in the field of data science and analytics, advancing their careers.

What Will You Learn?

During this training, participants will learn the following:
  • Data Exploration: The ability to thoroughly understand and analyze datasets.
  • Visualization Skills: Competency in visualizing and interpreting data effectively.
  • Statistical Foundations: The ability to understand and apply basic statistical concepts.
  • Examining Relationships: The capability to understand and interpret relationships between variables.
  • Handling Outliers and Missing Data: Strategies for identifying outliers and addressing missing data.
  • Statistical Tests: The ability to perform statistical hypothesis testing.
  • Time Series Analysis: Skills in analyzing and forecasting time series data.
  • Advanced Data Visualization: The capability to use advanced data visualization techniques.

Prerequisites

Who Should Attend?

  • This training is suitable for professionals working in positions such as data analyst, data scientist, business analyst, finance professional, marketing analyst, and similar roles. It is also beneficial for anyone looking to improve their data analysis skills and work with data.

Outline

Day 1: Understanding the basics and setting the foundation

Session 1: Introduction to Exploratory Data Analysis (EDA)
  • Understanding the concept and need for EDA in data science
  • Different stages of the data analysis process
  • The role of EDA in predictive modeling
  • Tools and techniques commonly used for EDA
Session 2: Getting familiar with data science tools
  • Introduction to Python for data analysis
  • Overview of data analysis libraries: NumPy, pandas
  • Hands-on: Setting up your environment, coding basics, data loading
Session 3: Working with data using pandas
  • DataFrames and Series: Creation, manipulation, indexing
  • Data cleaning: Dealing with missing values, duplicates
  • Data transformation: Column type conversion, binning
Session 4: Hands-on exercise
  • Perform basic EDA tasks on a sample dataset

Day 2: Diving deeper into EDA techniques

Session 1: Descriptive statistics
  • Measures of central tendency and dispersion
  • Understanding distributions: Normal, skewed
  • Z-Scores and Outliers
  • Correlation and Covariance
Session 2: Data Visualization Techniques
  • Introduction to Matplotlib, Seaborn
  • Plotting basic charts: Bar charts, line plots, scatter plots
  • Advanced charts: Box plots, histogram, pair plots, heatmaps
Session 3: Multivariate Analysis
  • Understanding multivariate analysis
  • Implementing PCA for dimension reduction
  • Visualizing high dimensional data
Session 4: Hands-on exercise
  • Implement descriptive statistics and visualization techniques on a sample
  • dataset

Day 3: Advanced EDA techniques and case study

Session 1: Time Series Analysis
  • Understanding time series data
  • Resampling and interpolation
  • Time series decomposition: Trend, Seasonality, Residual
  • Basic forecasting models
Session 2: Handling Missing Data
  • Reasons for missing data, types of missing data
  • Missing data imputation techniques
  • Evaluating imputation techniques
Session 3: Feature Engineering and Selection
  • Understanding feature engineering
  • Techniques for feature selection: Filter methods, wrapper methods, embedded
  • methods
  • Hands-on: Implement feature engineering and selection
Session 4: Case study and wrap-up
  • Full exploratory data analysis on a real-world dataset
  • Review key concepts, best practices, and pitfalls to avoid
  • Q&A and feedback sessio

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