Data Science Using Python For Beginners

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Data Engineering

Data Science Using Python For Beginners

Instructor

agenciple

Reviews 5.00 (3 Reviews)

Course Overview

A “Data Science Using Python for Beginners” This comprehensive journey introduces you to Python essentials for data manipulation and analysis. From mastering data cleaning techniques to engaging in exploratory data analytics, you’ll unravel the power of Python in the context of real-world projects. Gain insights into machine learning foundations and apply your newfound skills to hands-on activities, creating a portfolio that showcases your proficiency. With expert guidance and a focus on practical learning, this course is tailored for aspiring data scientists, coding enthusiasts, and beginners in the field.

🌐 Lesson Content:

In this comprehensive course, we’ll guide you through the fundamentals of data science using Python, breaking down complex concepts to make them accessible for beginners. From mastering data structures to exploring powerful algorithms, you’ll acquire the skills needed to extract meaningful insights from datasets.

πŸ’» What You Will Learn:

  • Python Essentials: Acquire proficiency in basic Python programming, laying a solid foundation for data manipulation and analysis.
  • Data Debate: Learn the art of producing and cleaning data, a critical step in any data science endeavor.
  • Exploratory Data Analytics (EDA): Engage in data visualization and statistical analysis to unveil patterns and trends within datasets.
  • Machine Learning Foundations: Gain an introduction to machine learning concepts and understand how to use basic frameworks for predictive analytics.
  • Real-World Projects: Apply your skills in hands-on activities, including data analysis and visualization, to build a robust portfolio showcasing your capabilities.

πŸ§‘β€πŸ« Why Agenciple Learning:

  • Experienced Instructors: Learn from industry professionals who bring real-world experience to the virtual classroom.
  • Flexible Learning: Experience course content at your own pace, including a mix of video lectures and interactive Q&A sessions.
  • Career Strategies: Gain insights into the field of data science and develop a strategic approach for potential career paths.

πŸ“ˆ Who Is This Course For:

  • Coding Enthusiasts
  • Beginners in Data Science
  • Aspiring Data Analysts
  • Anyone Seeking Practical Knowledge of Python

Keywords: Data Science, Python, Beginners, Agenciple Education, Interactive Learning, Career Opportunities, Data Analysis, Machine Learning, Visualization, Hands-on Projects.

What You'll Learn?

  • A Data Science course with Python offers a multitude of benefits, positioning learners at the forefront of the rapidly evolving field of data analytics. Python, as a programming language, is widely recognized for its versatility and ease of use, making it an ideal tool for manipulating and analyzing data. Through this course, participants gain a comprehensive understanding of key data science concepts, statistical analysis, and machine learning algorithms, empowering them to derive meaningful insights from complex datasets. The hands-on nature of Python facilitates practical application, allowing students to build and deploy their own data models. Additionally, Python's extensive ecosystem of libraries, such as NumPy, Pandas, and Scikit-learn, enhances efficiency and productivity in data manipulation and modeling tasks. As the demand for data-driven decision-making continues to rise across industries, acquiring proficiency in Python for data science not only opens up diverse career opportunities but also equips individuals with the skills to tackle real-world challenges using data-driven solutions. Overall, the course serves as a gateway to a dynamic and rewarding career in the burgeoning field of data science.

Course Content

  • Data Engineering For Everyone
    • Data Engineering and Big Data

      00:04:57
    • Data Engineers vs Data Scientist

      00:02:43
    • The Data Pipeline

      00:05:59
    • Data Structures

      00:04:38
    • SQL Databases

      00:05:27
    • Data Warehouses and Data Lakes

      00:04:38
    • Processing Data

      00:05:03
    • Scheduling Data

      00:04:33
    • Parallel Computing

      00:03:13
    • Cloud Computing

      00:04:26
    • We are the champions

      00:01:16
  • Introduction to Data Engineering
    • What is Data Engineering

      00:03:40
    • Tools of the Data Engineer

      00:04:20
    • Cloud Providers

      00:04:21
    • Databases

      00:04:44
    • What is parallel computing

      00:05:46
    • Parallel computation frameworks

      00:04:55
    • Workflow scheduling frameworks

      00:04:19
    • Extract

      00:05:58
    • Transform

      00:04:28
    • Loading

      00:04:12
    • Putting it all together

      00:04:45
    • Scheduling daily jobs

      00:03:57
    • Congratulation

      00:01:00
  • Streamlined Data Ingestion With Pandas
    • Introduction to Flat Files

      00:04:22
    • Modifying flat file imports

      00:04:35
    • Handling errors and missing data

      00:04:34
    • Introduction to spreadsheets

      00:04:47
    • Getting data from spreadsheets

      00:04:28
    • Modifying imports: true/false data

      00:04:45
    • Modifying imports : parsing dates

      00:05:03
    • Introduction to databases

      00:04:43
    • Refining imports with SQL queries

      00:04:16
    • More complex SQL queries

      00:04:08
    • Loading multiple tables with joins

      00:04:25
    • Introduction to JSON

      00:04:01
    • Introduction to APIs

      00:04:55
    • Working with nested JSONs

      00:04:51
    • Combining multiple datasets

      00:04:08
    • Wrap – up

      00:01:30
  • Writing Efficient Python Code
    • Welcome

      00:04:01
    • Building with built-ins

      00:04:02
    • The power of NumPy arrays

      00:04:47
    • Examining runtime

      00:05:10
    • Code profiling for runtime

      00:05:08
    • Code profiling for memory usage

      00:04:58
    • Efficiently combining, counting, and iterating

      00:06:16
    • Set theory

      00:04:39
    • Eliminating loops

      00:04:37
    • Writing better loops

      00:04:33
    • Intro to pandas Dataframes iteration

      00:05:13
    • Another iteration method: .itertuples()

      00:03:39
    • Pandas alternative to looping

      00:03:40
    • Optimal pandas iterating

      00:04:11
    • Congratulations

      00:01:26
  • Writing Functions With Python
    • Docstrings

      00:03:49
    • DRY and "Do One Thing"

      00:03:31
    • Pass by assignment

      00:03:55
    • Using contest managers

      00:03:24
    • Writing content managers

      00:03:28
    • Advanced topics

      00:03:38
    • Functions as objects

      00:03:56
    • Scope

      00:03:50
    • Closures

      00:03:44
    • Decorators

      00:04:24
    • Real - world examples

      00:03:25
    • Decorators and metadata

      00:02:32
    • Decorators that take arguments

      00:03:50
    • Timeout() : a real world example

      00:03:37
    • Great job!

      00:01:20
  • Unit Testing
    • Why unit test?

      00:04:12
    • Write a simple unit test using pytest

      00:04:40
    • Understanding test result report

      00:04:22
    • More benefits and test types

      00:04:21
    • Mastering assert statements

      00:04:23
    • Testing for exceptions instead of return values

      00:04:07
    • The well tested function

      00:04:36
    • Test driven development (TDD)

      00:03:20
    • How to organize a growing set of tests?

      00:05:02
    • Mastering test execution

      00:04:51
    • Expected failures and conditional skipping

      00:04:52
    • Continuous integration and code coverage

      00:05:01
    • Beyond assertion: setup and teardown

      00:04:50
    • Mocking

      00:04:15
    • Testing models

      00:04:17
    • Testing plots

      00:04:45
    • Congratulations

      00:02:13
  • Data Processing In Shell
    • Downloading data using curl

      00:04:32
    • Downloading data using Wget

      00:03:24
    • Advanced downloading using Wget

      00:03:07
    • Getting started with csvkit

      00:04:41
    • Filtering data using csvkit

      00:04:11
    • Stacking data and chaining commands with csvkit

      00:04:26
    • Pulling data from databases

      00:04:06
    • Manipulating data using SQL syntax

      00:03:43
    • Manipulating data using SQL syntax

      00:03:43
    • Pushing data back to database

      00:03:25
    • Python on the command line

      00:04:05
    • Python package installation with pip

      00:04:03
    • Data job automation with cron

      00:03:56
    • Course recap

      00:01:26
  • Introduction to Bash Scripting
    • Introduction and refresher

      00:04:02
    • Your first bash script

      00:02:32
    • Standard streams & arguments

      00:02:40
    • Basic variables in bash

      00:03:58
    • Numeric variables in bash

      00:04:02
    • Arrays in bash

      00:04:47
    • IF statements

      00:04:07
    • FOR loops & WHILE statements

      00:04:31
    • CASE statements

      00:03:02
    • Basic functions in bash

      00:02:59
    • Arguments, return values, and scope

      00:04:22
    • Scheduling your scripts with Cron

      00:04:19
    • Thanks and wrap up

      00:01:43
  • Object Oriented Programming with Python
    • What is OOP?

      00:04:40
    • Class anatomy: attributes and methods

      00:04:31
    • Class anatomy: the_init_constructor

      00:04:16
    • Instance and class data

      00:04:56
    • Class inheritance

      00:04:29
    • Customizing functionality via inheritance

      00:04:47
    • Operator overloading: comparison

      00:04:29
    • Operator overloading: string representation

      00:03:16
    • Exceptions

      00:04:13
    • Designing for inheritance and polymorphism

      00:04:33
    • Managing data access: private attributes

      00:03:43
    • Properties

      00:04:14
    • Congratulations

      00:02:15
  • Introduction to Airflow in Python
    • Introduction to Airflow

      00:03:56
    • Airflow DAGs

      00:03:59
    • Airflow web interface

      00:03:42
    • Airflow operators

      00:03:36
    • Airflow tasks

      00:03:43
    • Additional operators

      00:03:12
    • Airflow scheduling

      00:04:14
    • Airflow sensors

      00:03:42
    • Airflow executors

      00:03:30
    • Debugging and troubleshooting in Airflow

      00:03:13
    • SLAs and reporting in Airflow

      00:03:04
    • Working with Templates

      00:03:45
    • More templates

      00:04:02
    • Branching

      00:03:51
    • Creating a production pipeline

      00:02:44
    • Congratulations

      00:02:20
  • Building Data Engineering Pipelines in Python
    • Components of a data platform

      00:04:29
    • Introduction to data ingestion with singer

      00:04:30
    • Running an ingestion pipeline with singer

      00:04:17
    • Basic Introduction to PySpark

      00:04:45
    • Cleaning Data

      00:04:44
    • Transforming data with Spark

      00:04:25
    • Packaging your application

      00:04:34
    • On the importance of tests

      00:04:25
    • Writing unit tests for PySpark

      00:04:36
    • Continuous testing

      00:04:29
    • Modern day workflow management

      00:04:52
    • Building a data pipeline with Airflow

      00:04:44
    • Deploying Airflow

      00:04:22
    • Final thoughts

      00:00:59
  • Introduction To AWS Boto in Python
    • Intro to AWS and Boto3

      00:05:20
    • Diving into buckets

      00:04:20
    • Uploading and retrieving files

      00:04:22
    • Keeping objects secure

      00:05:02
    • Accessing private objects in S3

      00:04:53
    • Sharing files through a website

      00:04:56
    • Case study: Generating a report repository

      00:05:15
    • SNS topics

      00:04:36
    • SNS Subscriptions

      00:04:46
    • Sending messages

      00:03:19
    • Case study: Building a notification system

      00:05:03
    • Recognizing patterns

      00:04:46
    • Comprehending text

      00:04:58
    • Case study: Scooting Around!

      00:04:16
    • Wrap up

      00:02:19
  • Introduction To Relational Databases In SQL
    • Your first database

      00:04:14
    • Tables: At the core of every database

      00:03:38
    • Update your database as the structure changes

      00:04:14
    • Better data quality with constraints

      00:03:24
    • Working with data types

      00:03:20
    • The not - null and unique constraints

      00:03:34
    • Keys and superkeys

      00:03:11
    • Primary keys

      00:02:06
    • Surrogate keys

      00:03:47
    • Model 1: N relationships with foreign keys

      00:03:26
    • Model more complex relationships

      00:03:22
    • Referential integrity

      00:02:53
    • Roundup

      00:02:04
  • Database Design
    • OLTP and OLAP

      00:04:44
    • Storing data

      00:04:35
    • Database design

      00:04:28
    • Star and snowflake schema

      00:03:51
    • Normalized and denormalized databases

      00:04:06
    • Normal forms

      00:05:00
    • Database views

      00:03:32
    • Managing views

      00:03:43
    • Materialized views

      00:03:56
    • Database roles and access control

      00:03:59
    • Table partitioning

      00:04:00
    • Data integration

      00:03:50
    • Picking a Database Management System (DBMS)

      00:04:05
  • Introduction To Scala
    • A scalable language

      00:04:47
    • Scala code and the scala interpreter

      00:04:43
    • Immutable variables (val) and value types

      00:04:32
    • Mutable variables (var) and type inference

      00:03:54
    • Scripts, applications, and real - world workflows

      00:04:59
    • Functions

      00:04:12
    • Arrays

      00:04:39
    • Lists

      00:04:32
    • Scala's static type system

      00:04:45
    • Make decisions with if and else

      00:04:40
    • While and the imperative style

      00:04:25
    • Foreach and the functional style

      00:04:51
    • The essence of scala

      00:04:00
  • BigData Fundamentals With PySpark
    • Fundamentals of Big Data

      00:04:13
    • PySpark: Spark with python

      00:03:51
    • Use of lambda function in python - filter()

      00:03:45
    • Introduction to PySpark RDD

      00:03:58
    • RDD operations in PySpark

      00:05:19
    • Working with pair RDDs in PySpark

      00:04:28
    • More actions

      00:03:30
    • Introduction to PySpark DataFrames

      00:04:35
    • Interacting with PySpark DataFrames

      00:04:52
    • Interacting with DataFrames using PySpark SQL

      00:03:44
    • Data visualization in PySpark using DataFrames

      00:04:22
    • Overview of PySpark MLlib

      00:04:28
    • Introduction to collaborative filtering

      00:04:53
    • Classification

      00:04:57
    • Introduction to clustering

      00:04:49
    • Congratulations!

      00:03:22
  • Cleaning Data With PySpark
    • Intro to data cleaning with Apache Spark

      00:03:28
    • Immutability and lazy processing

      00:03:30
    • Understanding parquet

      00:04:28
    • DataFrame column operations

      00:04:10
    • Conditional DataFrame column operations

      00:02:39
    • User defined functions

      00:02:34
    • Partitioning and lazy processing

      00:03:37
    • Caching

      00:03:26
    • Cluster sizing tips

      00:03:27
    • Performance improvements

      00:03:29
    • Introduction to Data Pipelines

      00:02:59
    • Data handling techniques

      00:03:13
    • Data validation

      00:02:42
    • Final analysis and delivery

      00:01:47
    • Congratulation and next steps

      00:01:16
  • Introduction To Spark SQL In Python
    • Window function SQL

      00:03:27
    • Dot Notation and SQL

      00:03:11
    • Loading natural language text

      00:05:04
    • Moving window analysis

      00:04:56
    • Common word sequences

      00:04:23
    • Caching

      00:05:37
    • The spark UI

      00:05:07
    • Logging

      00:04:44
    • Query plans

      00:03:57
    • Extract transform select

      00:05:03
    • Creating feature data for classification

      00:05:06
    • Text classification

      00:04:55
    • Predicting and evaluating

      00:04:25
    • Recap

      00:01:15
  • Cleaning Data In SQL Server Database
    • Introduction to cleaning data

      00:04:53
    • Cleaning messy strings

      00:04:36
    • Comparing the similarity between strings

      00:04:46
    • Dealing with missing data

      00:04:13
    • Avoiding duplicate data

      00:04:34
    • Dealing with different date format

      00:03:05
    • Out of range values and inaccurate data

      00:04:11
    • Converting data with different types

      00:02:21
    • Pattern matching

      00:03:28
    • Combining data of some columns into one column

      00:02:56
    • Splitting data of one column into more columns

      00:03:24
    • Transforming rows into columns and vice versa

      00:04:26
    • Congratulations!

      00:01:19
  • Transactions and Error Handling In SQL Server
    • Welcome!

      00:04:06
    • Error anatomy and uncatchable errors

      00:03:23
    • Giving information about errors

      00:03:18
    • RAISERROR

      00:04:21
    • THROW

      00:03:41
    • Customizing error messages in the THROW statement

      00:04:11
    • Transactions

      00:05:04
    • @@TRANCOUNT and savepoints

      00:04:45
    • XACT_ABORT & XACT_STATE

      00:04:58
    • Transaction isolation levels

      00:05:12
    • READ COMMITTED & REPEATABLE READ

      00:04:58
    • SERIALIZABLE isolation level

      00:03:45
    • SNAPSHOT

      00:05:28
    • Congratulations!

      00:01:48
  • Building and Optimizing Triggers In SQL Server
    • Introduction

      00:04:28
    • How DML triggers are used

      00:03:30
    • Trigger alternatives

      00:03:06
    • AFTER triggers (DML)

      00:04:18
    • INSTEAD OF triggers (DML)

      00:03:18
    • DDL triggers

      00:04:20
    • Logon triggers

      00:02:38
    • Known limitations of triggers

      00:05:18
    • Use cases for AFTER triggers (DML)

      00:03:32
    • Use cases for INSTEAD of triggers (DML)

      00:03:46
    • Use cases for DDL triggers

      00:03:40
    • Deleting and altering triggers

      00:04:26
    • Trigger management

      00:05:37
    • Troubleshooting triggers

      00:05:02
    • Wrapping up

      00:01:17
  • Improving Query Performance In SQL Server
    • Introduction

      00:03:50
    • Aliasing

      00:03:14
    • Query order

      00:03:18
    • Filtering with WHERE

      00:03:10
    • Filtering with HAVING

      00:03:08
    • Interrogation after SELECT

      00:03:39
    • Managing duplicates

      00:03:36
    • Sub – queries

      00:03:35
    • Presence and absence

      00:02:30
    • Alternative methods 1

      00:03:29
    • Alternative methods 2

      00:03:51
    • Time statistics

      00:03:52
    • Page read statistics

      00:03:43
    • Indexes

      00:03:45
    • Execution plans

      00:03:51
    • Query performance tuning: final notes

      00:01:32
  • Introduction To MongoDB In Python
    • Welcome

      00:04:17
    • Finding documents

      00:03:59
    • Dot notation: reach into substructure

      00:03:24
    • The distinct() method

      00:03:24
    • Pre - filtering distinct values

      00:03:21
    • Matching array fields

      00:03:26
    • Distinct as you like it: Filtering with regular expressions

      00:03:41
    • Projection: Getting only what you need

      00:03:27
    • Sorting

      00:03:32
    • What are indexes?

      00:04:05
    • Limits and skips with sorts, Oh My!

      00:03:02
    • Intro to aggregation: From query components to aggregation stages

      00:03:07
    • Back to counting:

      00:04:00
    • Zoom into array fields with $unwind

      00:03:36
    • Something extra: $addFields to aid analysis

      00:03:09
    • Wrap – up

      00:00:54
  1. 5

    vaibhavpalhade98@gmail.com

Original price was: ₹4,999.00.Current price is: ₹499.00.
  • Duration 60:00:00
  • Lessons 330
  • Enrolled 13
  • Skill Intermediate
  • Last Update February 17, 2024