Data Science and Data Engineering course | Codebetter

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+91 88230 75444,

+91 99939 28766

data science internships for undergraduates - CodeBetter

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Overview
Course Content

Description

Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data. Additionally, CodeBetter IT Institute offers data science internships for undergraduates aiming to delve deeper into the practical applications of data science methodologies.

Course Content Overview:

- Python

- Numpy and Pandas

- Excel

- Power BI

- AI and Machine Learning

- RDBMS with MYSQL

- Major Project(AGILE SDLC, GitHub)

- Placement Support and Guidance

Python

Introduction, Variables, Data Types, Operators, type Conversion
Control structure - If else, switch, ternary operator, For loop, nested for loop, while loop, break, continue
Collection - List, Dictionary, Set
"functions, argument, return type, Arbitrary Arguments, *args, Keyword Arguments
"kwargs, Default value parameters, nested functions. Anonymous functions. Local vs global variables"
Introduction to OOP , Object , Class, Methods, initializer, self, static, List of object
Inheritance - single, multilevel, hierarchical , multiple, pass keyword
Runtime Polymorphism, Method overriding, super keyword, Abstraction, Abstract Class, ABC Module
Modules and Access specifiers, Exception Handling try, except, raise, else, finally, custom exception
Enumeration, Multithreading , File Handling, CSV read, write

Numpy and Pandas

Excel

Introduction to Business Analytics
Formatting Conditional Formatting and Important Functions
Analysing Data with Pivot Tables
Dashboarding
Business Analytics with Excel
Data Analysis Using Statistics

Power BI Syllabus

1: Quick Start Power BI Service
Get Power BI Tools
Introduction to Tools and Terminology
Dashboard in Minutes
Refreshing Power BI Service Data
Interacting with your Dashboards
Sharing Dashboards and Reports
2: Getting and Transforming Data with Power BI Desktop
Introduction to Power BI Desktop
Getting Data: Excel vs Power BI Desktop & Service
Naming for Q&A
DirectQuery vs Import Data
Recap and What's Next
3: Modeling with Power BI
Introduction to Modeling
Setup and Manage Relationships
Cardinality and Cross Filtering
Default Summarization & Sort by
Creating Calculated Columns
Creating Measures & Quick Measures
Recap and What's Next
4: Power BI Desktop Visualisations
Creating Visualisations
Color & Conditional Formatting
Setting Sort Order
Scatter & Bubble Charts & Play Axis
Slicers
Tooltips
Cross Filtering and Highlighting
Visual, Page and Report Level Filters
Drill Down/Up
Hierarchies
Constant Lines
Tables, Matrices & Table Conditional Formatting
KPI's, Cards & Gauges
Map Visualisations
Custom Visuals
Managing and Arranging
Drillthrough
Custom Report Themes
Grouping and Binning
Bookmarks & Buttons
Decomposition Tree
5: Power BI Service Visualisation Tools
Introduction to the Power BI Service
Standalone Tiles
Data Driven Alerts (Power BI Pro/Premium)
Quick and Related Insights
Custom Q&A
6: Publishing and Sharing
Sharing Options Overview
Publish from Power BI Desktop
Publish Reports to Web
Sharing Reports & Dashboards (Power BI Pro/Premium)
Workspaces (Power BI Pro/Premium)
Apps (Power BI Pro/Premium)
Printing, PDFs and Exporting to PowerPoint
Row Level Security (Power BI Pro)
Export Data from a Visualisation
Publishing for Mobile Apps
Sharing Options Summary
7: Refreshing Datasets
Understanding Data Refresh
Personal Gateway (Power BI Pro and 64-bit Windows)
Replacing a Dataset
Troubleshooting Refreshing
8: Power BI and Excel Together
Options for Publishing from Excel
Import Excel Power Query & Power Pivot Models
Analyze in Excel (Power BI Pro or Premium)
Excel Publish: Upload and Export to Power BI
Sharing Published Excel Dashboards (Power BI Pro or Premium)

Machine Learning

Introduction to Artificial Intelligence
Fundamentals of Machine Learning and Deep Learning
Machine Learning Lifecycle
Performance Metrics
Data Preprocessing
Feature Engineering
Supervised Learning-Classification
Unsupervised learning-Classification
Regression Analysis (Linear and Polynomial)
Logistic Regression
Naive Bayes classification
Decision Tree Algorithm
Random Forest Ensemble Algorithm
Support Vector Machine
Principal Component Analysis(PCA)
Unsupervised Learning with K-Means Clustering
Data Mining : Apyori Algorithem

Deep Learning

Introduction and Concept of Deep Learning
Deep Learning Platforms and Software Libraries
Neural Networks
Deep Neural Network
PyTorch and Deep Learning
TensorFlow and Deep Learning
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Introduction to Natural Language Processing
NLPs Techniques and Libraries
Speech Recognition Technique
Image Processing and classification Techniques
LLM Integration like ChatGPT

RDBMS with MYSQL

Fundamental SQL Statements
SQL Command Types
DDL, Normalization, Constraints
DML
Selection Commands
Selection Commands Filtering , Ordering
String Functions
Data and Time Functions
Mathematical Functions
Case and Decode
Aggregations
Group By Commands
Joins
Subqueries
Views and Index
User Access Control
Restore and Backup

Major Project(AGILE SDLC, GitHub)

Placement Support and Guidance

CourseFeatures

	Duration	8 - 10 Months
	Language	Hindi/English
	Certificate	Yes
	Expert trainers	Yes
	Flexible Timing	Yes
	Fee Installements	Yes

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