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Development Data Science

Data Science and Machine Learning For Beginners with Python

Learn to Analyse , Make Predictions, Explore data Frames,Clean and Visualize Data
Rating: 4.3 out of 54.3 (460 ratings)
29,002 students
Created by Bluelime Learning Solutions
Last updated 8/2020
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Install Jupyter Notebook Server
  • Create a new notebook
  • Explore Components of Jupyter Notebook
  • Understand Data Science Life Cycle
  • Use Kaggle Data Sets
  • Perform Probability Sampling
  • Explore and use Tabular Data
  • Explore Pandas DataFrame
  • Manipulate Pandas DataFrame
  • Perform Data Cleaning
  • Perform Data Visualization
  • Visualize Qualitative Data
  • Explore Machine Learning Frameworks
  • Understand Supervised Machine Learning
  • Use machine learning to predict value of a house
  • Use Scikit-Learn
  • Load datasets
  • Make Predictions using machine learning
  • Understand Python Expressions and Statements
  • Understand Python Data Types and how to cast data types
  • Understand Python Variables and Data Structures
  • Understand Python Conditional Flow and Functions
  • Learn SQL with PostgreSQL
  • Perform SQL CRUD Operations on PostgreSQL Database
  • Filter and Sort Data using SQL
  • Understand Big Data Terminologies.

Course content

6 sections • 78 lectures • 7h 59m total length

  • Preview02:13
  • Preview01:20
  • Installing Jupyter Notebook Server
    06:45
  • Running Jupyter Notebook Server
    03:10
  • Common Jupyter Notebook Commands
    07:28
  • Jupyter Notebook Components
    04:26
  • Jupyter Notebook Dashboard
    04:20
  • Jupyter Notebook User Interface
    05:44
  • Creating a new Notebook
    06:45

  • What is Python
    05:16
  • Python Expressions
    03:37
  • Python Statements
    04:42
  • Python Comments
    04:48
  • Python Data Types
    04:52
  • Casting Data Type
    02:57
  • Python Variables
    07:28
  • Python List
    09:33
  • Python Tuple
    07:11
  • Python Dictionaries
    10:17
  • Python Operators
    14:55
  • Python Conditional Statements
    08:03
  • Python Loops
    09:04
  • Python Functions
    07:59

  • What is Data Science
    08:51
  • Impact of Data Science
    04:05
  • Data Science life cycle
    02:04
  • Data Science Terminologies
    05:31
  • Kaggle Data Sets
    03:06
  • Probability Sampling
    09:37
  • Tabular Data
    08:00
  • Exploring Pandas DataFrame
    02:20
  • Manipulating a Pandas DataFrame
    12:57
  • What is Data Cleaning
    03:28
  • Basic Data Cleaning Process
    22:42
  • What is Data Visualization
    02:35
  • Visualizing Qualitative Data : Part 1
    12:00
  • Visualizing Qualitative Data : Part 2
    13:49

  • Installing Python
    05:28
  • Installing Pycharm on Windows
    04:24
  • Installing Pycharm on Macs
    03:30
  • Installing Anaconda
    05:44
  • What is Machine Learning
    10:13
  • Machine Learning Frameworks
    06:16
  • Machine Learning Vocabulary
    06:33
  • Supervised machine learning
    08:31
  • Where Machine Learning is used
    04:54
  • Creating a basic house value estimator
    11:17
  • Using Scikit-Learn
    08:41
  • Loading a dataset part 1
    08:29
  • Loading a dataset part 2
    03:05
  • Making Predictions part 1
    07:46
  • Making Predictions part 2
    03:45

  • What is SQL
    02:29
  • What is PostgreSQL
    03:44
  • Installing PostgreSQL on windows
    08:20
  • Installing PostgreSQL on Mac
    03:15
  • Connecting to a PostgreSQL Database
    07:30
  • Database Concepts
    04:31
  • Install Sample Database
    09:26
  • What is CRUD
    02:02
  • Data Types
    09:48
  • SQL CREATE TABLE Statement
    08:40
  • SQL INSERT Statement
    09:36
  • SQL SELECT Statement
    05:10
  • SQL UPDATE Statement
    07:48
  • SQL WHERE clause
    06:58
  • SQL ORDER BY Clause
    08:16

  • What is Big Data
    02:43
  • What is high volume
    03:03
  • What is high variety
    03:22
  • What is high velocity
    03:10
  • Google's Big Data Approach
    00:33
  • What is a cluster
    00:56
  • What is a Node
    01:29
  • Google File System
    01:38
  • Google's Big Table
    03:35
  • What is MapReduce
    06:14
  • Apache Hadoop
    02:41

Requirements

  • No Prior experience is required.
  • You'll need to install some software. We will show you how to do that step by step.

Description

Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information . Data is a fundamental part of our everyday work, whether it be in the form of valuable insights about our customers, or information to guide product,policy or systems development.   Big business, social media, finance and the public sector all rely on data scientists to analyse their data and draw out business-boosting insights.

Python is a dynamic modern object -oriented programming language that is easy to learn and can be used to do a lot of things both big and small. Python is what is referred to as a high level language. That means it is a language that is closer to humans than computer.It is also known as a general purpose programming language due to it's flexibility. Python is used a lot in data science. 

Machine learning relates to many different ideas, programming languages, frameworks. Machine learning is difficult to define in just a sentence or two. But essentially, machine learning is giving a computer the ability to write its own rules or algorithms and learn about new things, on its own. In this course, we'll explore some basic machine learning concepts and load data to make predictions.

We will also be using SQL to interact with data inside a PostgreSQL Database.


What you'll learn

  • Understand Data Science Life Cycle

  • Use Kaggle Data Sets

  • Perform Probability Sampling

  • Explore and use Tabular Data

  • Explore Pandas DataFrame

  • Manipulate Pandas DataFrame

  • Perform Data Cleaning

  • Perform Data Visualization

  • Visualize Qualitative Data

  • Explore Machine Learning Frameworks

  • Understand Supervised Machine Learning

  • Use machine learning to predict value of a house

  • Use Scikit-Learn

  • Load datasets

  • Make Predictions using machine learning

  • Understand Python Expressions and Statements

  • Understand Python Data Types and how to cast data types

  • Understand Python Variables and Data Structures

  • Understand Python Conditional Flow and Functions

  • Learn SQL with PostgreSQL

  • Perform SQL CRUD Operations on PostgreSQL Database

  • Filter and Sort Data using SQL

  • Understand Big Data Terminologies


A Data Scientist can work as the following:

  • data analyst.

  • machine learning engineer.

  • business analyst.

  • data engineer.

  • IT system analyst.

  • data analytics consultant.

  • digital marketing manager.










Who this course is for:

  • Beginners to Data Science
  • Beginners to Machine Learning
  • Beginners to Python
  • Beginners to SQL

Instructor

Bluelime Learning Solutions
Learning made simple
Bluelime Learning Solutions
  • 4.2 Instructor Rating
  • 19,419 Reviews
  • 475,175 Students
  • 229 Courses

Bluelime is UK based and creates quality easy to understand  eLearning  solutions .All our courses are 100% video based. We teach hands –on- examples  that teach real life skills .

Bluelime has engaged in various types of projects for fortune 500 companies and understands what is required to prepare students with the relevant skills they need.

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