Data Analysis (Analytics) Tutorial

Data Analysis or Data Analytics is studying, cleaning, modeling, and transforming data to find useful information, suggest conclusions, and support decision-making. This Data Analytics Tutorial will cover all the basic to advanced concepts of Excel data analysis like data visualization, data preprocessing, time series, data analysis tools, etc.

Data Analysis Tutorial

Data Analysis Process

Data Analysis is developed by the statistician John Tukey in the 1970s. It is a procedure for analyzing data, methods for interpreting the results of such systems, and modes of planning the group of data to make its analysis easier, more accurate, or more factual.

Therefore, data analysis is a process for getting large, unstructured data from different sources and converting it into information that is gone through the below process:

• Data Requirements Specification
• Data Collection
• Data Processing
• Data Cleaning
• Data Analysis
• Communication

Prerequisites for Data Analysis

To strong skill for Data Analysis we needs to learn this resources to have a best practice in this domains.

• Python For Data Analysis
• SQL For Data Analysis
• Python Data Visulization
• Data Analysis Datasets

Data Analysis Libraries

Pandas Tutorial

Learn Pandas to unlock powerful tools for data analysis in Python. This essential library offers versatile data structures like DataFrames, enabling efficient data manipulation, analysis, and visualization. Mastering Pandas will significantly enhance your ability to handle and extract insights from complex datasets, making it an indispensable skill for any data analyst or scientist.

Numpy Tutorial

Learn NumPy to master numerical computing in Python. This foundational library provides support for arrays, matrices, and high-level mathematical functions, making data manipulation and computation highly efficient. Understanding NumPy is crucial for performing advanced data analysis and scientific computing, and it serves as a cornerstone for many other data science libraries.

Understanding the Data

What is Data?

• Sample Vs Population Statistic
• Different Data Types:
  • Qualitative vs Quantitative (or categorical vs numerical)
  • Univariate Vs Multivariate Data types
  • Attributes of Nominal, Ordinal Vs Interval Data Types

Read and Loading the data set:

• Read Dataset with Pandas
  • Python | Read CSV
  • Export Pandas dataframe to a CSV file
  • Pandas | Parsing JSON Dataset
  • Exporting Pandas DataFrame to JSON File
  • Working with Excel files using Pandas
• Slicing, Indexing, Manipulating, and Cleaning Pandas Dataframe

Data Preprocessing:

Data preparation is a critical step in any data analysis or machine learning project. It involves a variety of tasks aimed at transforming raw data into a clean and usable format. Properly prepared data ensures more accurate and reliable analysis results, leading to better decision-making and more effective predictive models. This guide will cover key aspects of data preparation, including data formatting, data cleaning, outlier detection, data transformation, and data sampling.

• Data Formatting
  • Formatting float column of the data frame in Pandas
  • How to Check the Data Type in Pandas DataFrame?
  • How to change the Pandas datetime format in Python?
  • Convert the column type from string to datetime format in Pandas data frame
• Data Cleaning
  • Overview of Data Cleaning
  • Missing values
    • Working with Missing Data in Pandas
    • Drop rows from Pandas dataframe with missing values or NaN in columns
    • Count NaN or missing values in Pandas DataFrame
    • Handling Missing Values
    • Working with Missing Data
    • Handle Missing Data with Simple Imputer
    • Handle missing values of categorical variables
    • Replacing missing values using Pandas in Python
  • Outliers Detection
    • Boxplots
    • Detect and Remove the Outliers using Python
    • Z-score for outlier Detection
    • Density-based method for outlier Detection
      • Clustering-Based approaches for outlier detection
      • DBSCAN Clustering for outlier detection
    • Binning
      • Binning or Discretization
      • Binning method for data smoothing
    • Isolation Forest  for outlier detection
    • Support Vector Machine for outlier detection
• Data Transformation
  • Normalization and Scaling
    • Data Normalization
    • Difference between Data Normalization and Scaling
    • Data Normalization with Pandas
    • How to Standardize Data in a Pandas DataFrame?
    • Max-Min Normalization
    • Z-score Normalization
    • Decimal scaling normalization
    • Standard Deviation Normalization
    • Standardization
    • Log Transformation
    • Power transformation
• Data sampling:
  • Probability sampling
    • Simple Random Sampling
    • Clustered Sampling
    • Stratified Random sampling
    • Systematic Sampling
  • Non-Probability sampling

Exploratory Data Analysis

Exploratory Data Analysis (EDA) is also crucial step in the data analysis process that involves summarizing the main characteristics of a dataset, often with visual methods. The goal of EDA is to understand the data’s underlying structure, detect patterns and anomalies, test hypotheses, and check assumptions. EDA is essential for making informed decisions about data preprocessing, feature engineering, and modeling.

• What is Exploratory Data Analysis
  • Exploratory Data Analysis in Python
  • Exploratory Data Analysis in R 
• Univariate Data  EDA:
  • Measures of Central Tendency
  • Measures of spread
  • interquartile range (IQR)
  • ANOVA
  • Skewness
  • Calculate Skewness and Kurtosis in Python
  • Difference between Skewness vs kurtosis
  • Histogram
  • Interpretations of Histogram
  • Boxplots
  • Quantile-Quantile plots
    • Quantile-Quantile Plot in Python
    • Quantile-Quantile Plot in R
• Multivariate Data EDA
  • Cross-tabulation
  • Correlation & Correlation Matrix
  • Correlation and Covariance
  • Factor Analysis
  • Cluster Analysis
  • MANOVA(Multivariate Analysis of Variance)
  • Canonical Correlation Analysis
  • Correspondence Analysis
  • MultiDimensional Scaling
• Probability Distributions
  • Central Limit Theorem
  • Cumulative Distribution Functions
    • Calculate and plot a Cumulative Distribution function
    • Plot Cumulative Distribution Function in R
  • Probability Density Functions
  • Probability Density Estimation & Maximum Likelihood Estimation
  • Exponential Distribution
  • Normal Distribution
    • Normal Distribution function
      • Normal Distribution in Python
      • Normal Distribution in R
  • Binomial Distribution
    • Python – Binomial Distribution
    • Bernoulli Distribution in R
  • Poisson Distribution
  • P – Value
  • Z – Score
  • T-distribution
    • T-distribution
      • t-distribution in R
      • t-distribution in Python
  • Point Estimate
  • Confidence Intervals
  • Chi-Squared Tests
  • Hypothesis Testing
    • Hypothesis Testing Formula
    • One-Tailed Test & Two-Tailed Test

Time Series Data Analysis:

Time series data analysis involves examining data points collected or recorded at specific time intervals. This type of data is ubiquitous in various fields, such as finance, economics, environmental science, and many others. The primary goal is to understand the underlying structure and patterns to make accurate predictions or decisions.

• Define Time Series Data
• Data and Time function in Python
• Time Series Data Plotting
• Deal with missing values in a Time series
• Moving Averages in Time Series Data
• Stationarity in Time Series Data
• Seasonality Detection in Time Series Data
• Trend in Time Series Data
• Testing for Mean Reversion
• Augmented Dickey-Fuller Test
• What is Autocorrelation?

Data Analysis Tools:

• Excel Tutorial
• Tableau Tutorial
• Power BI Tutorial

Need for Data Analysis

Data analytics is significant for business optimization performance. An organization can also use data analytics to make better business decisions and support analyzing customer trends and fulfillment, which can lead to unknown and better products and services. Executing it into the business model indicates businesses can help reduce costs by recognizing more efficient modes of doing business. 

Applications of Data Analysis

• Better decision-making: The Key advantage of data analysis is better decision-making in the long term. Rather than depending only on knowledge, businesses are increasingly looking at data before deciding. 
• Identification of potential risks: Companies in today’s world succeed in high-risk conditions, but those environments require critical risk management processes, and extensive data has contributed to developing new risk management solutions. Data can enhance the effectiveness of actual simulations to predict future risks and create better planning.
• Increase the efficiency of work: Data analysis allows you to analyze a large set of data and present it in a structured way to help reach your organization’s objectives. Possibilities and progress within the organization are reflected, and activities can increase work efficiency and productivity. It enables a culture of efficiency and collaboration by allowing managers to share detailed data with employees.
• Delivering relevant products: Products are the oil for every organization, and often the most important asset of organizations. The role of the product management team is to determine trends that drive strategic creation, and activity plans for unique functions and services.
• Track customer behavioral changes: Consumers have a lot to choose from in products available in the markets. Organizations have to pay attention to consumer demands and expectations, So to analyze the behavior of the customer data analysis is very important.

FAQs on Data Analysis

Q.1 What are the four types of Data Analysis?

Answer: There are four types of data Analysis:

• Descriptive
• Diagnostic
• Predictive
• Prescriptive

Q.2 Why is data analytics so important?

Answer: Data analytics is more than simply showing numbers and figures to the administration. It is about analyzing and understanding your data and using that information to drive actions. Data analytics displays the patterns and trends within the data, which strengthen or otherwise remain unknown. 

Q.3 What are the tools useful for data analysis?

Answer: Some of the tools useful for data analysis include: 

• RapidMiner 
• KNIME 
• Google Search Operators 
• Google Fusion Tables 
• Solver 
• NodeXL 
• OpenRefine 
• Wolfram Alpha 
• io 
• Tableau, etc.

Q.4 What are the differences between Data Mining and Data Profiling?

Data Mining	Data Profiting
Data mining is the procedure of finding suitable data that has not yet been determined before.	Data profiling is done to estimate a dataset for its uniqueness, logic, and consistency.
In data mining, raw data is converted into useful information.	It cannot identify incorrect data values.

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