Data Analytics

What is Data Analytics?

Data Analytics is the process of collecting, cleaning, exploring and interpreting data to answer business questions. It helps teams monitor performance, understand trends and make evidence-based decisions.

• Compiler: Python is a programming language widely used for data analysis, automation and machine learning.
• Header file: Provides declarations for functions and types.
• Function: A reusable block of code that performs a task.
• Use cases: Business analytics, forecasting, customer insights, scientific research and many other data-driven fields.

Basic workflow

Define the question → gather data → clean and validate → analyse → visualise → communicate recommendations.

Data Analytics fundamentals

A Data Analytics workflow starts with a business question and dataset, then moves through cleaning, exploration, calculation, visualisation and communication.

#include <stdio.h>

int main(void) {
    char name[40] = "Ravi";
    int age = 21;
    printf("Hello %s, age %d\n", name, age);
    return 0;
}

Practise importing a spreadsheet, checking data types, summarising values and handling missing data in Excel or Power Query.

Excel for Data Analysis

Reusable formulas, structured tables and documented steps help keep analysis clear, repeatable and easier to review.

#include <stdio.h>

int add(int a, int b) {
    return a + b;
}

int main(void) {
    printf("Sum: %d\n", add(4, 6));
    return 0;
}

• Declaration: A DataFrame stores tabular data in rows and columns.
• Definition: A function groups steps into a reusable operation.
• Parameters: Parameters let a function work with different inputs.
• Recursion: A notebook records code, outputs and explanatory notes in one workflow.

Exploratory Data Analysis

Exploratory Data Analysis (EDA) uses summaries, tables and charts to understand patterns, spot anomalies and explore relationships before drawing conclusions.

#include <stdio.h>

int main(void) {
    for (int i = 1; i <= 5; i++) {
        printf("%d\n", i);
    }
    return 0;
}

Use summary tables, PivotTables, histograms and scatter plots to inspect data. Check whether missing values or outliers may affect conclusions.

Pandas, NumPy and Visualisation

SQL helps retrieve and aggregate records from databases, while Power Query supports data transformation. Visualisation tools help communicate patterns clearly.

#include <stdio.h>

int main(void) {
    int score[] = {70, 85, 92};
    int *ptr = score;
    printf("First score: %d\n", *ptr);
    return 0;
}

Practise array indexing, string handling, address-of (&) and dereference (*) operators. Avoid accessing memory outside array bounds.

Statistics essentials

Statistics help summarise data and quantify variation, patterns and uncertainty.

#include <stdio.h>

struct Student {
    int id;
    char name[30];
};

int main(void) {
    struct Student s = {101, "Asha"};
    printf("%d: %s\n", s.id, s.name);
    return 0;
}

Review central tendency, spread, probability, sampling and the difference between correlation and causation.

Power BI dashboard basics

Data analytics uses descriptive and diagnostic methods to explain performance and identify patterns. A good workflow includes validation and careful choice of metrics.

#include <stdio.h>
#include <stdlib.h>

int main(void) {
    int *values = malloc(3 * sizeof *values);
    if (values == NULL) return 1;
    values[0] = 10;
    printf("%d\n", values[0]);
    free(values);
    return 0;
}

Understand descriptive, diagnostic and trend analysis, and compare results using clearly defined business metrics.

File handling

Data import and export allow analysts to work with CSV, Excel and database sources. Validate column names, missing values and data types after loading.

#include <stdio.h>

int main(void) {
    FILE *file = fopen("notes.txt", "w");
    if (file == NULL) return 1;
    fputs("Practise C every day.\n", file);
    fclose(file);
    return 0;
}

Practise importing CSV and Excel files, exporting cleaned data and documenting the steps used to transform it.

Testing and project workflow

1. Define the analytical question and success criteria.
2. Validate source data and document assumptions.
3. Check missing values, duplicates and unexpected values.
4. Inspect intermediate tables and chart the most important patterns.
5. Record methods, limitations, metrics and recommendations.

Prefer reproducible notebooks, reliable validation, clear charts and honest explanations of uncertainty.

Data Analytics glossary