Data Analyst

Data Analyst Mastery Program

Course Overview:

The Data Analyst Mastery Program is a comprehensive, hands-on course designed to equip students with the essential skills and tools needed to excel in the field of data analytics. Whether you're a beginner or looking to upskill, this course will transform your analytical abilities, preparing you for a career in data-driven decision-making.

Key Highlights:

• Duration: 80 Days (Including live sessions, assignments, and project work)

• Mode: Online/Offline (as applicable)

• Level: Beginner to Advanced

• Tools Covered: Python, SQL, Power BI, and Excel

Data Analytics Course Syllabus

• Introduction to python

• Environment setup & start programming

• Python Conditional Statements, Loops and File Handling

• Core Objects and Advanced Data Structures; Functions and Lambdas

• The Object Oriented Side of it

Introduction to Data Analysis with Python

• What is data analysis?

• Overview of Python and its data analysis libraries (NumPy, pandas, Matplotlib, Seaborn)

• Setting up your Python environment

Data Preprocessing and Cleaning

• Importing data from various sources (CSV, Excel, SQL)

• Exploring and understanding the dataset

• Handling missing data: imputation techniques

• Dealing with outliers and anomalies

• Data transformation: normalization, standardization

• Data integration and manipulation using pandas

Exploratory Data Analysis (EDA)

• Descriptive statistics: mean, median, mode, variance, etc.

• Histograms, box plots, scatter plots

• Correlation analysis and heatmaps

• Univariate and bivariate analysis

• Data visualization using Matplotlib and Seaborn

Data Visualization

• Advanced data visualization techniques: bar plots, line plots, pie charts, etc.

• Interactive visualizations using Plotly

• Geospatial visualization

• Effective data storytelling and communication

Statistical Analysis

• Sampling techniques and the Central Limit Theorem

• Hypothesis testing: t-tests, chi-square tests, ANOVA

• Confidence intervals and p-values

• Interpreting statistical results

Machine Learning Algorithms for Data Analysis

• Introduction to machine learning

• Feature engineering and selection

• Linear regression: simple and multiple regression

• Logistic regression for classification

• Decision trees and random forests

• Model evaluation metrics: R-squared, MAE, RMSE, accuracy, precision, recall, F1-score

• Model assumptions and diagnostics

Time Series Analysis

• Introduction to time series data

• Time series components: trend, seasonality, noise

• Decomposition techniques

• Time series forecasting methods: moving average, ARIMA,
  exponential smoothing

• Implementing time series analysis in Python

Advanced Topics

• Dimensionality reduction techniques (PCA, t-SNE)

• Clustering algorithms (K-means, hierarchical clustering)

• Advanced statistical techniques (non-parametric tests, ANCOVA)

Real-World Projects and Case Studies

• Applying data analysis concepts to real datasets

• Solving data analysis challenges and problems

• Creating a portfolio of data analysis projects