Data Science can be leveraged by different fields. However, it can be daunting to know how to start and go about creating a data science project. Questions such as how to begin, what steps to follow, etc. can be very difficult to answer, especially for a beginner. Therefore, in this blog, we brought you Data Science Life Cycle which will help you complete a project from scratch.
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Introduction to Data Science LifeCycle
Data science is an interdisciplinary field combining computer science and mathematics to extract valuable insights from large amounts of data. It has revolutionized problem-solving in computer applications by enabling organizations to leverage vast amounts of data effectively. Previously, businesses struggled to extract useful information from their data, leading to decisions based on limited insights and predictions. However, with the rise of Data Science, companies can now utilize accumulated data to make informed decisions and develop predictive models. The demand for skilled Data Science professionals has surged as companies recognize the importance of these capabilities. To address Data Science problems, professionals follow a systematic process consisting of distinct steps.
Data Science is very popular and in demand, therefore professionals who have been dealing with data science projects have come up with a process that can be used to solve Data Science problems.
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The Six Stages of the Data Science Life Cycle
Framing the Problem
Whenever we are trying to solve a Data Science problem, we must first understand the scope and depth of the problem that we are trying to solve.
The first stage of the Data Science Life Cycle, “Framing the Problem,” aligns with Albert Einstein’s famous quote emphasizing the importance of problem definition. In this stage, data scientists spend a significant amount of time understanding and clarifying the problem at hand before diving into solution development. By investing a substantial portion of their time in problem framing, they gain a comprehensive understanding of the underlying challenges, goals, constraints, and potential impacts. This initial step ensures that the subsequent data analysis and modeling stages are focused, relevant, and effective in providing actionable insights and solutions. It highlights the significance of problem definition as a critical foundation for successful data-driven decision-making.
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Key Questions must be asked in framing the problem:
To make sure that we are solving the right problem, the most important thing is to ask as many questions as possible to get a clear sense of what the stakeholders wish for the product or service. For example, when building a movie recommendation engine, we can begin the work by asking questions such as:
- What kind of a system would the company like to build?
- What kind of data is available for us to use?
- How many movies are there in the library?
- How many movies should be there in a recommendation?
- How are these recommendations going to be used?
Only after getting clear answers to these questions and more, we should begin building the system. This step will ensure that we are solving the same problem that the company wants us to solve.
Data collection is a vital step in the Data Science life cycle as all decisions are based on the data we have. It is crucial to ensure that the collected data is of high quality and sufficient for solving the problem at hand. Issues like data faults, inaccuracies, or insufficiency can arise when gathering data from multiple diverse sources. Combining data from these sources into a single dataset can also be challenging.
To ensure the data’s quality, there are various strategies that can be implemented. One option is to engage customers directly in the data collection process, soliciting their input through surveys or interviews to gain valuable insights. Another technique involves web scraping, extracting data from websites as a supplementary source of information. These measures contribute to obtaining reliable and comprehensive data for analysis and decision-making purposes.
Key points to be remembered while collecting data:
- Ensure data is gathered from reliable sources to maintain data integrity.
- Verify the accuracy and completeness of the collected data to avoid making decisions based on faulty information.
- Collect data directly from relevant stakeholders, such as customers or users, to obtain firsthand insights.
- Implement proper data governance and data management practices to maintain data quality and consistency.
- Consider the legal and ethical aspects of data collection, respecting privacy and data protection regulations.
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Processing the Data
Data processing is a crucial step in the data science life cycle as it ensures the quality and reliability of the collected data before further analysis. Without proper processing, the data may contain errors or inconsistencies that can lead to inaccurate results and flawed decision-making.
Importance of Data Processing:
The significance of data processing can be understood in various aspects:
- Error Identification and Resolution: Data processing plays a crucial role in detecting and resolving common errors such as missing values, outliers, incorrect values, and inconsistent date formats. This ensures the data’s reliability and suitability for analysis.
- Data Integration: When data is collected from multiple sources, it often comes with different formats, structures, or discrepancies. Data processing facilitates the integration of diverse datasets, ensuring compatibility and coherence in the final dataset.
- Data Transformation: Processing involves transforming the data into a standardized format, which simplifies the analysis and interpretation process. Tasks like data normalization, converting timestamps to a consistent time zone, and handling categorical variables are carried out during data processing to enhance the quality and utility of the data.
Common Data Processing Issues and Solutions:
- Missing Values: Fill missing values with appropriate techniques such as mean, median, or regression imputation, or consider dropping the column if it contains too many missing values.
- Outliers: Identify outliers and decide whether to remove them or apply statistical techniques to handle them appropriately.
- Inconsistent Date Formats: Standardize date formats by converting them into a common format or considering the time zone information to adjust the timestamps accordingly.
- Data Collection Errors: If faulty data sources are identified, either discard the data or recollect it from reliable sources.
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Exploring the Data
Data exploration is one of the most important and time-consuming steps in the life cycle of Data Science. We may be spending anywhere from a day to multiple weeks exploring data. The data exploration step is done to make sure that we can extract some patterns from our data, which can lead us to solve our problem.
- Fraud Detection in Financial Transactions: In the financial industry, exploring data plays a vital role in fraud detection. Analysts examine transactional data, including transaction amounts, timestamps, geographical locations, and user behavior patterns. By exploring the data, they can identify abnormal patterns, suspicious activities, or anomalies that may indicate fraudulent behavior. These insights help in developing effective fraud detection algorithms and systems to mitigate risks and protect customers’ financial interests.
- E-commerce Customer Segmentation: In the data exploration phase of an e-commerce company, analysts delve into customer data to discover patterns and segments. They examine diverse customer characteristics, including demographics, purchase history, browsing habits, and engagement metrics. This exploration process enables them to extract valuable insights such as identifying lucrative customer segments, understanding buying preferences, and customizing marketing tactics to better cater to individual customer needs.
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Analyzing the Data
In this step, we try and get a deeper understanding of the data we have collected and processed. We use statistical and numerical methods to draw inferences about the data and to identify the relationship between multiple columns in our dataset. We can also use visualizations to better understand and summarize the data using images, graphs, charts, plots, etc.
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These tools allow us to build a model that can make predictions or perform classification on a given dataset. We get to learn how to better understand our data and the patterns underlying it to convert them into useful information. Using these tools, we can also determine how different columns are related to each other by finding out their correlation. Using these insights, we can determine how to solve the different problems that we are tackling.
Social Media Engagement Analysis- During the data exploration stage, analysts may analyze social media data to understand user engagement and preferences. Using charts and graphs, they can visualize key metrics and patterns. For example:
- A bar graph can show the number of likes, comments, and shares for different posts, helping identify the most engaging content.
- A line chart can display the trend in follower growth over time, indicating the effectiveness of marketing campaigns.
- A pie chart can illustrate the distribution of audience demographics, allowing for targeted content creation.
Customer Satisfaction Analysis
In the data exploration stage, analysts may analyze customer feedback data to assess satisfaction levels. Charts and graphs can provide visual representations of the findings. For instance:
- A stacked bar graph can show the percentage of positive, neutral, and negative customer reviews, providing an overall sentiment analysis.
- A line chart can display the average ratings over time, helping identify any fluctuations in customer satisfaction.
- A scatter plot can depict the relationship between customer satisfaction scores and specific product features, highlighting areas for improvement.
Using the insights gained from all the previous steps of our data science process, we now have to be able to consolidate the results so that they can be analyzed and understood by stakeholders. That is, once we have created visualizations, analyzed the data, and have concluded, we need to create documents that justify our conclusions by describing the insights and visualizations.
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We hope this blog has helped demystify the phases of the Data Science life cycle, which can help you tackle your next Data Science project systematically. Here, we have discussed a standard process that is in use. However, you can create your processes by adding steps to this standard one or removing steps based on your problems and their requirements.
If you have any queries or questions you can write to them on our Data Science community forum.