Learn Data Analytics
Learn Data Analytics
Learn Data Analytics – Every day, we create and interact with massive amounts of data—whether it’s through social media, online shopping, or even searching for information. But raw data alone doesn’t mean much. To truly understand and use it, we need Data Analytics.
Data Analytics is the process of examining data to find useful patterns and insights. Businesses use it to make smarter decisions, improve customer experiences, and predict future trends.
We will be covering the following things in this course:
- Introduction to Data Analytics
- Statistics in DA
- Tableau
- Learn ML in Data Analytics
- Data Analysis with Excel
- Difference between AI, ML, DA, BA
- And Many More.
These are the most important topics for preparation of any Data Analytics Interview, so learn it accordingly.
Data Analysis and Data Analytics
Data Analysis refers to the process of examining, organizing, and interpreting raw data to find patterns, trends, and insights. It focuses on answering specific questions using past data.
- Example: A company analyzing last year’s sales data to identify the best-selling products.
Data Analytics is a broader field that includes Data Analysis, predictive modeling, and decision-making. It involves using advanced techniques like machine learning, automation, and statistical models to predict future trends and optimize strategies.
- Example: An e-commerce platform using customer data to recommend products based on past purchases.
Introduction to Data Analytics
Statistics in Data Analytics
Statistics in Data Analytics is the backbone of analyzing and interpreting data. It involves collecting, organizing, and studying data to identify patterns, trends, and relationships.
- Statistical methods help in making sense of large datasets, drawing conclusions, and making predictions.
- Key concepts like mean, median, standard deviation, and probability are widely used in analytics.
- Introduction to Statistics in Data Analytics
- Descriptive vs. Inferential Statistics
- Types of Data in statistics for data analytics
- Levels of Measurement: (nominal, ordinal, interval, ratio)
- Descriptive Statistics
- Measures of Central Tendency
- Measures of Variability
- Basic Bivariate analysis
- Foundational understanding of probability
- Common probability distributions
- Discrete Distributions
- Continuous Distributions
- Random Variable vs Probability Distribution
- Central Limit Theorem
- Skewness & Kurtosis
- Non- Normality of Data
- Statistical Inference
- Hypothesis Testing
- Degrees of Freedom
- Different Types of Errors
- Confidence Intervals
- One Tailed vs Two Tailed Test
- Z tests and T Tests
- Parametric vs. Non-parametric Tests
- Significance level and P-value
- Analysis of Variance (ANOVA)
- Chi-Square Test
- Corelation
- Covariance
- Bias , Variance, Tradeoff
Tableau in Data Analytics
- Introduction to Tableau interface and navigation
- Various data sources (Excel, CSV, databases, etc.)
- Different types of data fields (dimension vs. measure)
- Advanced Visualization Techniques
- Calculated fields and parameters for advanced calculations
- Implementing filters, groups, and sets for data segmentation
- Understanding table calculations and level of detail expressions
- Delving into Data Analysis and Manipulation
- Implementing advanced data blending
- Data joining techniques
- Predictive analysis
- Statistical functions and analytics in Tableau
- Advanced data preparation techniques within Tableau
- Understanding best practices for dashboard layout and design
- Compelling data stories through the dashboard
- Integrating Tableau with other tools
- Publishing and sharing Tableau visualizations on Tableau Server
- COVID-19 Data Visualization
Mastering Machine Learning in Data Analysis
- Introduction to Machine Learning with Python
- Understand the basics of machine learning
- Regression
- Classification
- Clustering algorithms
- Data Preprocessing
- Feature scaling and normalization
- Categorical Variables – One-hot encoding or label encoding
- Split data into training and testing sets
- Regression Analysis
- Linear Regression and its variants
- Decision tree regression
- Classification Analysis
- Logistic Regression and its application in binary classification
- Support Vector Machines (SVM)
- Naive Bayes classifiers
- Regression
- Classification
- Clustering algorithms
- Data Preprocessing
- Feature scaling and normalization
- Categorical Variables – One-hot encoding or label encoding
- Split data into training and testing sets
- Regression Analysis
- Linear Regression and its variants
- Decision tree regression
- Classification Analysis
- Logistic Regression and its application in binary classification
