Best Data Analytics Course in Chennai – Code&Debug

Python

• Variables, Numbers, Strings
• Lists, Dictionaries, Sets, Tuples
• If condition, for loop
• Functions, Lambda Functions
• Modules (pip install)
• Read, Write files
• Exception handling
• Classes, Objects

Numpy, Pandas, Data Visualization

• Tech Skills
  • Numpy
  • Pandas, Matplotlib, Seaborn
• Core/Soft Skills

Statistics and Math for Data Science

• Basics: Descriptive vs inferential statistics, continuous vs discrete data, nominal vs ordinal data
• Basic plots: Histograms, pie charts, bar charts, scatter plot etc.
• Measures of central tendency: mean, median, mode
• Measures of dispersion: variance, standard deviation
• Probability basics
• Distributions: Normal distribution
• Correlation and covariance
• Central limit theorem
• Hypothesis testing: p value, confidence interval, type 1 vs type 2 error, Z test, t test, ANOVA

Exploratory Data Analysis (EDA)

• [ https://www.kaggle.com/code?searchQuery=exploratory+data+analysis ]
• Use the above link to search for exploratory data analysis notebooks.

SQL

• Basics of relational databases
• Basic Queries: SELECT, WHERE LIKE, DISTINCT, BETWEEN, GROUP BY, ORDER BY
• Advanced Queries: CTE, Subqueries, Window Functions
• Joins: Left, Right, Inner, Full
• No need to learn database creation, indexes, triggers etc. as those things are rarely used by data scientists.

MongoDB and Tableau

• MongoDB:
  • MongoDB ecosystem
  • MongoDB structures
  • MongoDB shell vs MongoDB server
  • Data Formats in MongoDB
  • Working with MongoDB compass
  • Working on Scheme Designing
• Tableau:
  • Dashboard and Stories
  • Visual Analytics
  • Custom geocoding
  • Polygons Maps
  • VMS and Background image

Machine Learning

• Preprocessing:
  • Handling NA values, outlier treatment, data normalization
  • One hot encoding, label encoding
  • Feature engineering
  • Train test split
  • Cross validation
• Model Building:
  • Types of ML: Supervised, Unsupervised
  • Supervised: Regression vs Classification
  • Linear models
  • Linear regression, logistic regression
  • Gradient descent
  • Nonlinear models (tree-based models)
  • Decision tree
  • Random forest
  • XGBoost
  • Model evaluation
  • Regression: Mean Squared Error, Mean Absolute Error, MAPE
  • Classification: Accuracy, Precision-Recall, F1 Score, ROC Curve,
  • Confusion matrix
  • Hyperparameter tunning: GridSearchCV, RandomSearchCV
  • Unsupervised: K means, Hierarchical clustering, Dimensionality reduction (PCA)

PowerBI | Big Data and Spark Analytics

• PowerBI:
  • Overiew of the workflow in PowerBi desktop
  • Introducing the different views of data Mode
  • Working with Time Series
  • Scatterplots and Bi Reports
  • Connecting Dataset with Power BI
• Big Data and Spark Analytics:
  • Introduction to Hadoop and Big Data
  • What is Spark
  • Getting to know PySpark Hands-on: Map, reduce
  • Polygons Maps
  • VMS and Background image

Excel

• Getting started with Excel
• Using Excel toolbars: Hiding, Displaying and Moving
• Toolbars Switching Between Sheets in a Workbook
• Entering Data Values and using AutoComplete
• Inserting and Deleting Cells, Rows and Columns
• Working with the form Menu
• Creating and Working with Charts
• Changing a Chart Type and Working with Pie Charts
• Data Analysis and Pivot Tables

Time Series and Forecasting

• Introduction to Time Series Forecasting
• Introduction to ARIMA Modes, Case Study on Time Serie

Certainly! The classes are beginner-friendly and tailored to guide you from scratch. So, there is no need to know anything about coding beforehand.

It’s a live class, allowing real-time interaction with instructors and solutions to leetcode questions for logic buildup.

Yes, recordings will be available for missed sessions. All the recordings of LIVE classes will be available.

Yes, a refund is possible within 14 days of the class starting date. No questions asked.

No prerequisites needed. The class will start from the basics and will go till advance.