Introduction

Data is shorthand for “information,” and whether you are collecting, reviewing, and/or analyzing data this process has always been part of Head Start program operations. Students' enrollment into the program requires many pieces of information. The provision of health and dental services includes information from screening and any follow-up services that are provided. All areas of a Head Start program – content and management – involve the collection and use of substantial amounts of information.

Prerequisites

1. What is Data Analysis
2. Who is a Data Analyst.
3. Data Analysis Life Cycle
4. Improving the model functionality.

What Is Data Analysis

Data analysis is the processing of data to yield useful insights or knowledge.
• Data processing involves finding, loading, cleaning, manipulating, transforming, modeling, and visualizing the data.
• The knowledge may be used for scientific discovery, business decision-making, or a variety of other applications.

A data analyst is a person who uses tools and applications to transform raw data into a form that will be useful.

A data analyst is a person who uses tools and applications to transform raw data into a form that will be useful.

From this perspective, we present a data analysis process that includes the following key components:
• Purpose
• Questions
• Data Collection
• Data Analysis Procedures and Methods
• Interpretation/Identification of Findings
• Writing, Reporting, and Dissemination; and
• Evaluation
We have also found, from our review of the literature, that there are many different ways
of conceptualizing the data analysis process. We can make a basic distinction between a
linear approach and a cyclical approach; in this Handbook we provide examples of both.

Data Analytics Life Circle

Data is precious in today’s digital world environment. It goes through several life stages, including creation, testing, preprocessing, consumption, and reuse.

These stages are mapped out in the Data Analytics Life Cycle for professionals working on data analytics initiatives. Each stage has its own significance and characteristics.

1. Define the Problem In the data analysis process, the most challenging phase is to define the problem that needs to be solved. Deciphering the root cause of an issue requires a profound understanding of a business’ needs and aspirations, and involves a deep dive into metrics, KPIs, and other crucial indicators.

This stage involves conducting initial analyses in order to gain valuable insights. It is crucial that this stage is done properly, as it lays a strong foundation for the entire data analysis process.

2. Data Collection
After defining the problem, a data analyst then determines the most suitable data to address that question. The types of data they usually collect here include

• Quantitative data – like marketing figures – - or Qualitative data – like customer reviews.

Data types can be further categorized into 3 main groups:
first-party data (or data collected directly by an organization)
second-party data (or first-party data collected by one organization used by another), or third-party data (or data aggregated from multiple sources by a third party).

If the necessary data is incomplete or missing, a data analyst will be responsible in this step for devising a strategy for data collection. This includes different methods like surveys, social media monitoring, website analytics tracking, and online tracking in general.

3. Data Cleaning
Freshly-collected data in its raw form is typically unorganized and messy. Before proceeding with the necessary analysis, that data must be cleaned up. In order to clean data, errors, duplicates, and outliers must be removed, along with any irrelevant data that does not contribute to the analysis being done.

Additionally, the data must be restructured in a more meaningful manner depending on the type of analysis being done. Missing values must be filled in, too, in order to make the data more accurate. Data that is highly accurate can provide more valuable insights in the data analysis process.

4. Data validation
After it is cleaned, the data must be validated. This process involves verifying whether the data meets the specific requirements of the analysis being performed.

5. Perform Exploratory Data Analysis (EDA)
Exploratory Data Analysis(EDA) is the main step in the process of various data analysis. It helps data to visualize the patterns, characteristics, and relationships between variables. Python provides various libraries used for EDA such as NumPy, Pandas, Matplotlib, Seaborn, and Plotly.

6.Build the Model

Model building is an essential part of data analytics and is used to extract insights and knowledge from the data to make business decisions and strategies. In this phase of the project data science team needs to develop data sets for training, testing, and production purposes. To do this, dataset needs to be divided into two parts;

• Training dataset
• Test dataset

Note: Based on the dataset quality and quantity of the data one may choose to divide his dataset into three parts training and testing and validation data.

To divide the dataset, Python sklearn library which helps in dividing the dataset into training and testing datasets is used to perform the train and test split. Here a data analyst will choose the ratio by which he/she want to divide the dataset by default it 8:2 meaning 80% and 20% for training and testing respectively.

7. Share the outcome
After conducting the analysis and extracting important insights, the final step lies in effectively communicating these findings to those who initiated the project in the first place.

While it is essential to interpret the data accurately, it is equally important to be able to present those findings clearly and concisely. A data analyst is often working with marketing executives or stakeholders who are on time constraints and may not possess much technical expertise.

8. Model Deployment
After performing a success EDA, the next final stage is to deploy the model into a real-world system or application to automatically generate predictions or perform specific tasks.

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