Simplifying Data Analysis with Python: A Beginner’s Guide

Welcome to a beginner’s guide on data analysis in Python. In this blog post, we will be covering the basic concepts and techniques needed to get started with data analysis, including the definition of data analysis, why Python is useful for data analysis, the difference between data analysis and data science, and the different types of data analytics, it’s packages and tools used in data analytics.

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∘ Table of Contents:
· 1. What is Data Analysis?
· 2. Why use Python for Data Analysis?
· 3. Difference between data analyst and data scientist
· 4. Types of Data Analytics
· 5. What exact process a data analyst will follow?
· 6. Python Packages used in Data Analysis?
· 7. What tools are mainly used in data analytics?

1. What is Data Analysis?

Companies generally collect large amount of data, but in it’s raw form, which does not provide any meaningful information to us. This is where data analysis comes in.

In simple terms, Data Analysis is the process of converting raw data into useful & meaningful information. It involves certain process like collecting, cleaning, analyzing & transforming data to extract meaningful & actionable insights, patterns & trends that help companies make effective business decisions.

The results are then presented in a simple & comprehensive way so that the stakeholders can take action immediately.

2. Why use Python for Data Analysis?

Python is a powerful programming language for data analysis because of its flexibility, huge library collections, graphics, visualizations, and built-in data analytics tools. Some of the key libraries for data analysis in Python include:

• Numpy: a library for numerical computing in Python.
• Pandas: a library for data manipulation and analysis.
• Matplotlib and Seaborn: a library for data visualizations.
• Scikit-learn: a library for machine learning.

3. Difference between data analyst and data scientist

Data analysis & data science both deals with data, but they have some difference in their focus and skill sets.

Data Analysis:

Data Analysis is the process of analyzing & interpreting data using statistical and computational methods. The main focus of data analytics is to extract insights & knowledge from data to help organizations make informed decisions on statistical and visualization techniques.

Data Science:

Data science is a broader field, that includes data analytics, but it also encompasses other fields like machine learning, artificial intelligence, and computer science. The main focus of data science is to use algorithms and models to solve complex problems and make predictions using data.

4. Types of Data Analytics

Descriptive Analytics: is the process of analyzing historical data to understand patterns & trends. It provides insights into “what has happened” in the past and helps organizations understand the current state of affairs.

• Descriptive analytics uses “data visualization tools” to visually display the data in a clear & easily understandable manner, that is by using charts, graphs, and maps to show the trends as well as dips & spikes.
• Descriptive analytics is helpful for showing how the trends vary based on changes in time.
• Example: Imagine you are analyzing your company’s data and you got to know that there is a seasonal surge in the sales of one of the products let’s say for a mobile phone console. Here descriptive analytics can tell you that “This mobile phone console experiences an increase in sales in June, July, and August each year”.

Diagnostic Analytics: is used to identify the cause of a particular outcome or event.

• Continuing the aforementioned example: suppose after digging into the demographic data of mobile phone console users, we got to know that the user's age is between 8 and 18, however, the customer's age is between 40 & 60. Analysis of customer survey data reveals that one primary motivator for customers to purchase the mobile phone console is to gift their children. The spike in sales in summer may be due to the summer holidays that include gift-giving.
• Diagnostic analytics often include advance statistical techniques such as regression analysis, hypothesis testing, etc.

Predictive Analytics: is utilized to make forecasts based on previous data. It involves developing models to predict future outcomes.

• For Example: Spikes in mobile phone console in the months June, July & August every year for the past decade provides you with ample data that the same trend will also occur next year.
• Predictive Analysis includes advance statistical and machine learning techniques like regression, neural network, and decision trees.

Prescriptive Analytics: is used to identify the best course of action to take based on predictions made by predictive analytics models. It involves using optimization algorithms and decision-making tools to determine the optimal actions that will achieve specific objectives.

5. What exact process a data analyst will follow?

The several stages involved in the process of data analysis:

1. Defining the Problem: This is the first step of data analysis process, where you need to identify why you are conducting data analysis and what questions or challenges you hope to solve. You will then need to identify what kind of data you require and from where it will come from that is the source of data.

Let’s explain this in simple terms with the help of example:

There are 2 scenarios:

1. Suppose your Manager has given you the data and along with the data he has also given you some questions. Your task is to get the answers for these questions.
2. The second scenario is: You have been given data & you are asked to analyze the data & tell how can the company make profit within next year.

The second scenario is a bit difficult coz you have to ask questions like:

1. What features will contribute to my analysis?
2. What features are not important for my analysis?
3. Which of the features have a strong correlation?
4. Do I need Data Preprocessing?
5. What kind of feature manipulation/engineering is required?

2. Data Collection: With a clear question in my, the next step is to collect data.

• Data collection starts with “primary sources” known as “internal sources”. This is basically structured data gathered from CRM software, ERP systems, marketing automation tools, etc.
• Then comes “secondary sources”, also known as “external sources”. This is both “structured & unstructured data” that can be gathered from government portals, social media APIs, data published by major organizations like UNICEF and World Health Organization.

3. Data Cleaning: Once you have collected the data, your next task is to clean the data thoroughly. This is the most important step in data analysis. Data cleaning involves removing duplicates, removing any unnecessary or unwanted data, ,handling missing data and dealing with outliers that may affect your data.

4. Exploratory Data Analysis: The next step after data cleaning is to perform exploratory data analysis (EDA) to identify patterns and relationships in the data. This involves using visualizations & statistical techniques to summarize and understand the data. This step also ties in with the 4 types of data analytics that we have alredy discussed (i.e.; descriptive, diagnostic, predictive & prescriptive).

5. Data Visualization: In this step, the data is finally transformed into valuable business insights which are then presented using data visualization tools like charts, graphs & dashboards, which will help to communicate the insights to stakeholders and decision-makers.

6. Communicating Results: After the final analysis is made, the results are compiled into a report which is shown either in person or in form of PPT or in blog post. This report is presented to the stakeholders to inform decision-making. At this stage, you collaborate with the stakeholders on how to move forward. This is also a good tie to highlight any limitations to your data analysis and to consider what further analysis might be conducted.

These process are iterative and often involves going back and forth between the different stages until a satisfactory solution is reached.

6. Python Packages used in Data Analysis?

Python is a collection of functions and methods that allows you to perform a lot of activities without writing any code. Python libraries usually contain built-in modules with different functionalities that can be directly used.

Some commonly used Python packages include:

• NumPy: This package is used for numerical computing in Python. It provides tools for working with arrays. Using this package you can work with multidimensional arrays or matrix and can even do advanced mathematical operations in a easy and faster way.
• Pandas: This package provide easy-to-use data structure and data analysis tools, which is used for data cleaning, preparation & exploration.
• Matplotlib: This package is used to create 2D graphs, charts and maps by using python script.
• Seaborn: This package is built on top of Matplotlib. Seaborn provides advanced visualization options like heat-maps, time series and violin plot.
• SciPy: It is collection of scientific computing tools for Python. It includes modules for optimization, integration, linear algebra and more.
• Scikit-learn: This package provides a wide range of statistical models & tests for Python. It includes tools for regression, classification, clustering, and dimensionality reduction.
• Statsmodels: This package provides a wide range of statistical models & tests for Python. It includes tools for regression time-series analysis and hypothesis testing.
• PySpark: This is a python library for Apache Spark. It is used for big data processing and analysis.

All these packages can be used individually or in combination to perform a variety of data analysis tasks in python.

7. What tools are mainly used in data analytics?

Data Analysts use a variety of tools to perform data analysis, some of them are:

1. Programming languages: Data analysts often use programming languages such as Python, R or SQL to manipulate and analyze the data.
2. Data visualization tools: such as Tableau, Power BI are used to create cisula representations of data.
3. Statistical softwares: Programs like SPSS and SAS are used for statistical analysis and modeling.
4. Excel: It is a popular tool for data anlaysis especially for smaller datasets.
5. Cloud-based tools: such as as Amazon Web Service (AWS), Google Cloud Platform (GCP) are used to store and analyze large datasets.
6. Machine learning tools: Tools like Scikit-learn, TensorFlow are used for machine learning and predictive modeling.
7. Data Cleaning tools: Tools like OpenRefine and Trifacta are used for data cleaning and preparation.
8. Collaborative tools: Tools like Github, Jupyter Notebook and Slack are used for collaboration and sharing of code.

The specific tools used by a data analyst may vary depending on the industry, the size of the organization, and the type of data being analyzed.

That’s it for now:

That it for today’s session. In the next session we will be discussing about NumPy and Pandas in detail along with the code.

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