Summary of Data Analytics Full Course 2025 | Data Analytics Tutorial | Data Analyst Course | Simplilearn

Summary of "Data Analytics Full Course 2025 | Data Analytics Tutorial | Data Analyst Course | Simplilearn"

Data is generated from every online action (clicks, purchases, etc.).
Data analytics transforms raw data into actionable insights for smarter business decisions.
By 2025, data analytics skills will be essential across industries like tech, healthcare, finance, and retail.
High demand for data professionals; learning analytics can lead to a lucrative career.
The course covers basics, tools, visualization, interpretation, and real-world business applications.
Mention of a professional certificate program combining data analytics and generative AI with IIT Guwahati and IBM.

Data analysis is like detective work to solve business problems using data.
Data analytics involves collecting, cleaning, examining data to gain insights.
Data analysts translate complex data into understandable business language using tools like Excel, Python, dashboards.
Four main types of data analytics:
- Descriptive Analytics: What happened? (e.g., website visits last month)
- Diagnostic Analytics: Why did it happen? (e.g., sales drop causes)
- Predictive Analytics: What might happen? (e.g., sales forecast)
- Prescriptive Analytics: What should we do? (e.g., discounts or product launches)

Excel is a powerful tool for data analysis beyond simple tables.
Data can be structured, semi-structured, or unstructured.
Key Excel functions for data analysis:
- SUMIF, SUMIFS for conditional summations.
- COUNTIF, COUNTIFS for conditional counts.
- Examples demonstrated for summing units sold by region, revenue by item, filtering sales reps, etc.
Importance of dynamic formulas over manual filtering for scalable analysis.

Structured Data: Databases (MySQL, PostgreSQL), CSV files, Excel spreadsheets.
Semi-structured Data: APIs exchanging data in JSON/XML.
Unstructured Data: Web scraping to extract data from websites.
File formats: text-based (CSV, JSON) and binary (databases, images).
Data storage options: local, network, cloud, databases; considerations include scalability, cost, access, security.

Clean data is critical for accurate insights.
Common issues: missing values, duplicates, outliers.
Techniques:
- Detect missing values (e.g., using lambda functions in Python).
- Handle missing data by removal or imputation (mean values).
- Normalize data ranges for better model performance.
Combining/merging datasets using pandas concat and merge functions.

Python is a powerful tool for data analysis, faster and more flexible than Excel.
Key Python libraries:
- pandas: Data manipulation, DataFrame and Series structures.
- NumPy: Numerical computing with n-dimensional arrays.
- SciPy: Scientific computing (linear algebra, integration).
- Matplotlib: Data visualization.
- scikit-learn: Machine learning.
Demonstration of creating Series and DataFrames, importing data, and performing exploratory data analysis (EDA).
Use of Jupyter notebooks for interactive coding.
Steps in Python data analysis:
- Import libraries.
- Load data.
- Inspect data (head(), describe()).
- Visualize data (histograms, plots).
- Data wrangling and cleaning.

Focuses on summarizing and describing data.
Key metrics:
- Central tendency: mean, median, mode.
- Dispersion: range, variance, standard deviation.
- Shape: skewness (asymmetry), kurtosis (peakness).
Helps understand data distribution and identify outliers.