The Ultimate Guide to Data Analytics.

Data analysis involves a series of steps and methods that help transform raw data into meaningful insights. Forging a data analysis career involves gaining a competitive edge given the challenges in the evolving market using a combination of programming, statistical methods, and real-world applications.

This guide highlights basic processes and examples essential for beginner level data analysis track.

Foundations of Data Analysis

Data Structures

Data structures are a specific way of organizing data in a specialized format on a computer so that it can be organized, processed, stored and retrieved quickly and effectively, essential for large datasets.

Key Operations in Data Structures

1. Searching - locating a piece inside a specific data structure. This may be done in structures like arrays and lists.

2. Sorting - ordering data elements in a data structure in a certain order; ascending or descending.

3. Insertion - adding new data to the structure.

4. Updating and deleting - modifying or deleting existing data structure parts.

Data Types

Understanding data types helps determine the kind of operations one can perform on the data. Different data types require different analysis techniques, visualization and data preparation.

a) Qualitative Data: Represents non-numerical information that describes the qualities or characteristics of a variable.

• Nominal Data: Categories without a specific order or ranking (e.g., Gender, Types of Fruits).

• Ordinal Data: Categories with a defined order or ranking, but without measurable differences between ranks (e.g., Education Level, Customer Satisfaction Ratings).
  b) Quantitative Data: Represents numerical values that measure the quantity or magnitude of a variable.

• Discrete Data: Countable values (e.g., Number of Students, Cars Sold).

• Continuous Data: Measurable values that can take any number within a range (e.g., Height, Temperature).

c) Date and Time Data: Specific points in time or durations, crucial for time-based analysis and forecasting.

d) Compound Data Types: Combines multiple data types within a single dataset or variable to store complex data.

• Arrays: Homogeneous data structures for numerical computations.
• Lists: Ordered, mutable collections of elements that can contain different data types.
• Tuples: Ordered, immutable collections, often used for storing related data.
• Dictionaries: Unordered collections of key-value pairs, useful for fast lookups.

Data Collection and Preparation

Data collection involves distinguishing between primary and secondary data sources. Primary data can be collected using web scraping tools like Scrapy, Beautiful Soup, and Selenium, or through APIs. Secondary data is obtained from existing or external databases. Github

import scrapy
from selenium import webdriver
from selenium.webdriver.chrome.service import Service
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.common.by import By
from scrapy.http import HtmlResponse

Data Analysis Techniques

Each technique is unique to specific nature of data and objectives one has.

• Descriptive analysis - this provides a summary of historical data, quantitatively. Central tendency (mean, median, mode)

Python

import numpy as np
import pandas as pd

df.read_csv = age.csv #assuming file name is age
#
mean_value = np.mean(df)
print(mean_value)
#
median_value = np.median(df)
print(median_value)

#
mode_value = stats.mode(df)
print(mode_value.mode[0])

Variability (range, variance, standard deviation)
SQL

SELECT variance(column_name) AS Variance_value FROM table_name;

--std deviation
SELECT stddev(column_name) AS Stddev_value FROM table_name;

Frequency distribution (tables and charts)

import matplotlib.pyplot as plt

#table
freq_table = pd.Series(df).value_counts()
print(freq_table)

#chart (Histogram)
plt.hist(data, bins=5, edgecolor='black')
plt.title('Frequency Distribution')
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.show()

• Inferential analysis - makes inferences and predictions about a population based on sample of data. Hypothesis testing : t-tests, chi-square tests Regression analysis : linear regression ANOVA

from scipy.stats import f_oneway

# Sample data
group1 = [12, 15, 14, 10, 12]
group2 = [22, 25, 21, 23, 20]
group3 = [32, 35, 31, 30, 29]

f_stat, p_value = f_oneway(group1, group2, group3)
print("F-Statistic:", f_stat)
print("P-Value:", p_value)

Confidence intervals

import numpy as np
import scipy.stats as stats

data = [12, 15, 12, 13, 18, 19, 21, 18, 20, 17, 16, 22, 24, 20]
confidence = 0.95
mean = np.mean(df)
n = len(df)
std_err = stats.sem(df)
h = std_err * stats.t.ppf((1 + confidence) / 2, n - 1)
confidence_interval = (mean - h, mean + h)
print("Confidence Interval:", confidence_interval)

• Exploratory Data Analysis(EDA) - Exploring and identifying patterns, trends, and relationships within the data. Data visualization - scatter plots, histograms. box plots

Summary statistics

Correlation matrices

correlation_matrix = df.corr()
print(correlation_matrix)

Heatmaps

sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Heatmap')
plt.show()

• Text analysis - deriving meaningful information from text data; such as keywords, phrases, sentiments or patterns using statistical and machine learning techniques.

• Natural language processing(NLP) - A method for analyzing and interpreting human language data.

Data Analysis Process

• Define the objective; what you want to achieve with the analysis.

• Data Collection; from various sources using respective methods.

• Data Cleaning; by handling missing values and inconsistencies.

• Exploratory Data Analysis; to understand and discover patterns.

• Data Analysis; applying appropriate analysis methods based on the objectives.

• Interpret Results; translating to actionable insights and providing recommendations.

• Data Visualization and Reporting; to present findings in a clear and accessible way.

Follow this guide to develop a foundational skill set that covers basic aspects of data analysis, from foundational knowledge to techniques and applications. This approach ensures you are well-equipped to tackle real-world data challenges and make impactful data-driven decisions.