Data analysis roadmap

Figure 1: Roadmap DA. Reference

Table of Contents

1. Resources
2. Fundamentals
3. Key Concept
4. Introduction to web development
5. DMP platform

Resources

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• Introduction
• Google Data Analytics Professional Certificate

Fundamentals

• Introduction to Data Analytics -Data Analytics is a core component of a Data Analyst’s role. The field involves extracting meaningful insights from raw data to drive decision-making processes. It includes a wide range of techniques and disciplines ranging from the simple data compilation to advanced algorithms and statistical analysis. As a data analyst, you are expected to understand and interpret complex digital data, such as the usage statistics of a website, the sales figures of a company, or client engagement over social media, etc. This knowledge enables data analysts to support businesses in identifying trends, making informed decisions, predicting potential outcomes - hence playing a crucial role in shaping business strategies.
• Types of Data Analytics: Data Analytics has proven to be a critical part of decision-making in modern business ventures. It is responsible for discovering, interpreting, and transforming data into valuable information. Different types of data analytics look at past, present, or predictive views of business operations. Data Analysts, as ambassadors of this domain, employ these types, to answer various questions:
  • Descriptive Analytics (what happened in the past?)
  • Diagnostic Analytics (why did it happened in the past?)
  • Predictive Analytics (what will happen in the future?)
  • Prescriptive Analytics (how can we make it happen?)

Key Concept

In the realm of data analysis, understanding some key concepts is essential. Data analysis is the process of inspecting, cleansing, transforming, and modeling data to discover useful information and support decision-making. In the broadest sense, data can be classified into various types like nominal, ordinal, interval and ratio, each with a specific role and analysis technique. Higher-dimensional data types like time-series, panel data, and multi-dimensional arrays are also critical. On the other hand, data quality and data management are key concepts to ensure clean and reliable datasets. With an understanding of these fundamental concepts, a data analyst can transform raw data into meaningful insights. These Key concept includes:

• Data Collection:
  • https://www.questionpro.com/blog/data-collection-methods/
  • https://www.simplilearn.com/what-is-data-collection-article
• Cleanup
  • https://support.microsoft.com/en-gb/office/top-ten-ways-to-clean-your-data-2844b620-677c-47a7-ac3e-c2e157d1db19
  • https://www.youtube.com/watch?v=jxq4-KSB_OA
• Exploration
  • https://www.heavy.ai/learn/data-exploration
  • https://www.youtube.com/watch?v=OY4eQrekQvs
• Visualization
  • https://www.tableau.com/en-gb/learn/articles/data-visualization
  • https://www.youtube.com/watch?v=loYuxWSsLNc
• Statistical Analysis
  • https://www.simplilearn.com/what-is-statistical-analysis-article
  • https://www.youtube.com/watch?v=XjMBZE1DuBY

Foundation

• Data Collection

  • Working with Databases
  • CSV Files in Data Collection for Data Analysts
  • APIs and Data Collection
  • Web Scraping

• Data Cleaning

  • https://www.tableau.com/learn/articles/what-is-data-cleaning#:~:text=tools%20and%20software-,What%20is%20data%20cleaning%3F,to%20be%20duplicated%20or%20mislabeled.

• Descriptive Analysis

• Data Visualization

  • Charting, create chart, use-cases.
  • Pivot table.
  • DMP platform

• Statistical Analysis

Introduction to web development

• How the web works
• Basic HTML, CSS, JS
• Using APIs
• Events handling
• DMP use-cases

DMP platform