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Data Sources

Lesson 10/31 | Study Time: 18 Min
Course: Data Science Process for Beginners

Once data requirements are defined, the next step is identifying where the data will come from. Data sources are generally categorized into internal and external sources. Each type offers unique advantages, challenges, and use cases. Understanding available sources ensures that models are comprehensive, reliable, and relevant.

Internal Data Sources

Internal sources are all data generated within the organization. This includes databases, CRM systems, operational records, logs, and financial documents. Internal data is often the most valuable because it directly reflects the company’s customers, operations, and performance.

Common internal data sources include:


Internal data is usually rich, high-value, and directly aligned with business goals. However, it may suffer from inconsistencies, missing fields, or lack of standardization across departments. Achieving cross-system integration is often the biggest challenge.

External Data Sources

External datasets complement internal data and broaden analytical capability. These sources include publicly available datasets, commercial third-party data, partner-provided data, and social or market insights.
Common external data sources include

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himanshu singh

himanshu singh

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Class Sessions

1- What is Data Science? 2- Importance of Methodology 3- Overview of Common Frameworks 4- Roles and Applications in the Industry 5- Business Understanding 6- Defining objectives and questions 7- Framing Data Science Problems 8- Working with IDEs 9- Identifying data requirements 10- Data Sources 11- Basics of Data Collection & Ethics 12- Data Exploration Basics 13- Handling Missing or Inconsistent Data 14- Data Cleaning Essentials 15- Introduction to data wrangling 16- Introduction to Analytical Thinking 17- Overview of Analytical Methods 18- Introduction to Key Tools :- Python and Excel 19- Summary Statistics 20- Measures of Spread (Variance, Standard Deviation) 21- Central Tendency and Dispersion 22- Interpreting Basic Statistical Outputs 23- Introduction to Data Analysis 24- Basic Data Visualization: Charts, Graphs, Plots 25- Extracting Insights From Data 26- Structuring a Data Science Report 27- Presenting Insights Visually and Textually 28- Introduction to Storytelling with Data 29- Ethical Considerations in Data Science 30- Reviewing the Data Science workflow 31- Emerging Trends and Where to Go next