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Applied Data Science with Python Specialization
Rating: 4.4 out of 5(170 ratings)
1,397 students

Applied Data Science with Python Specialization

Master Class of Data Science with Case Studies using Python
Created byPiyush Dave
Last updated 1/2022
English

What you'll learn

  • The course provides path to become a data scientist
  • Problem Solving Approach
  • Impress interviewers by showing an understanding of the data science concept
  • Make powerful analysis
  • Python Basic to Advance Concept
  • Python Libraries for Data Analysis such Numpy, Scipy, Pandas
  • Python Libraries for Data Visualization such Matplotlib, Seaborn, Plotlypy
  • Case Studies of Data Science with Coding

Course content

14 sections99 lectures13h 21m total length
  • Why to join this course?1:32
  • Introduction of Python and Python Libraries7:58

    # Intro of python and python libraries:

    Python is an example of high-level language. Programs written in high level language have to be processed before they can run. It takes less time to write, they are shorter and easier to read, and they are more likely to be correct. They are portable means they can be run on different kinds of computers with few or no modifications. A python library is a reusable chunk of code that you may want to include in your programs or projects. Libraries basically describes collection of modules.

  • Meet Trainer for this Course1:17
  • Set Up – Python IDLE and Google Colab6:47

    #Set up environment google colab:

    Colab is a python development environment that runs in the browser using google colab. In shortly, it allows you to write and execute python in your browser. The document you are reading is not static, but an interactive environment called a colab notebook that lets you write and execute code. It is a virtual machine you can access directly. To runs commands at the VM’s terminal, prefix the line with an exclamation mark.

  • Brief Introduction of Python2:25
  • Data Type and Variable, Keywords10:23

    #data type and variables, keywords:

    A value is a basic thing in program works with letters or number. These values are assigned with variables. They belong to different types such as integer and “ hello” is a string because it contains letters. Variable is a capability of storing some kind of data. It then can be modified, stored or displayed. Variables are basically referred as containers that hold information. Keywords are pre-defined, reserved words which has special meaning. They are a part of syntax.

  • How to take input?3:40

    #Take inputs:

    Input can come in various ways, for example from a database, another computer, mouse clicks and movements or from the internet. For this purpose, python provides the function named input(). There are two functions raw_input() which reads an input and returns a string and other one is input() which reads it and return the python type.

  • How to produce output?3:34

    #print statement:

    The print() function prints the specified message to the screen, or other standard output devices. The message can be a string, or any other object, the object can be converted into a string before written to the screen. Sometimes it may happen you need to print blank line in your python program for that “\n” is used.

  • Introduction to List, Tuple, Dictionary, Set3:07

    #Intro to lists, tuples and dictionaries:

    The Lists are very similar to arrays. It is a sequence of values. The values in list are called elements or sometimes items. List is created by placing all items inside a square bracket. The Tuples is similar structure as list. In tuples we cannot change the elements after assigning. Tuples are used for grouping data and enclosed in parathesis. Dictionary deals with an unordered set of key and value pairs. It contains data in curly braces. The pair is known as item and are separated by colon.

  • List Operations in details9:43

    #list operations:

    List are basically the sequence of values. The values in list are called the items or elements. List operations consist of append method which basically add or append the object to the list. Then there are many such methods such as index which returns the lowest index in list that object appears and many more such operations.

  • Tuple Operations in details6:05

    #tuple operations:

    Tuples are sequences just like list. The main difference is it cannot be changed unlike list. Tuples operations contains concatenation which refers to combining contents of two tuples using + sign. The result will be a single tuple. Other is in operator which checks the element is the tuple. And many more like iteration and so on.

  • Set Operations in details3:39

    #set operations:

    Set is an unordered collection of items. Every set element is unique and cannot be changed i.e immutable. Set can be used to carry out mathematical set operations like union, intersection, difference. We can do this with methods.

  • Dictionary Operations in details3:34

    #dictionary operations:

    Each key is separated from its value by colon, the items are separated by commas and the whole thing is enclosed in curly braces. The dictionary contains clear method which refers to removing all the elements from dict. There are many such methods copy, get, items and so on.

  • String Operations8:01

    #string operations:

    String in python are a set of characters represented in the form of single quotation mark or double quotation marks. There are various string operations such as capitalize() which represents first letter of string. Then lower() which converts string into lowercase. Then many more like index(str,beg=0,end), find(), len(string).

  • Operators in details8:13

    #Operators:

    Operators are special symbols that represent computations like addition and multiplication. The values the operator uses are called operands. When a variable name appears in the place of an operand, it is replaced with its value before the operation is performed.

  • Data Type Conversion11:11

    #datatype conversion:

    Value is one thing works with a letter or number. This values are assigned with variables. There are two types integer and string. Type conversion is converting one type of data to another type. Which is also known as type casting.

  • Importance of Indentation6:21

    #importance of indentation:

    Indentation refers to spaces of the beginning of the code line. In python indentation is very important to indicate a block of code.

  • Random Number, Range Function7:15
  • Sequential, Selection & Repetition -for, while, break, continue, if-elif else11:18

    #sequencial, selection and repetition:

    Sequential execution is when statements are executed one after another in the order. Selection used for decisions, branching-choosing between two or more alternative paths. Repetition is used for looping that is repeating a piece of code multiple times in a row.

  • Math Library4:06

    #math library:

    Library basically describes a collection of many modules. The math library provides us access to some common math functions and constants, which can be use throughout the code for more complex mathematical computations.

Requirements

  • It start with Basics
  • Only a passion for Learning
  • All software used in this course is either available for Free or as a Demo version
  • This course is intended for absolute beginners in programming

Description

"Comprehensive Data Science Masterclass: Python, Libraries, and ML Algorithms"


Dive into the heart of data science with our all-encompassing masterclass covering Python basics, advanced Python concepts, and essential libraries including Numpy, Scipy, Pandas, Matplotlib, Seaborn, and Plotlypy. Explore the intricate steps of Data Science, from introduction to project initiation, supported by real-life case studies that illuminate the path forward.


Course Highlights:


1. Python Proficiency:

   - Master Python's core and advanced features, essential for data analysis and machine learning.


2. Library Mastery:

   - Dive deep into Numpy, Scipy, Pandas, Matplotlib, Seaborn, and Plotlypy for robust data manipulation and visualization.


3. Data Science Journey:

   - Understand the complete data science life cycle, from data collection to insightful analysis and modeling.


4. Machine Learning Insights:

   - Explore Supervised and Unsupervised Learning, along with vital concepts like Train-Test Split, Machine Learning Models, and Model Evaluation.


5. ML Algorithms with Scikit Learn:

   - Delve into machine learning algorithms such as Linear Regression, Logistic Regression, SVM, K Means, KNN, Naïve Bayes, Decision Tree, and Random Forest through practical case studies.


Why Enroll?


This course is your gateway to a thriving career in data science. With a focus on hands-on experience and practical applications, you'll navigate the complexities of data analysis, visualization, and machine learning effortlessly. Whether you're a beginner aiming to start a rewarding career or a professional looking to enhance your data science skills, this masterclass equips you with the expertise needed to succeed in the dynamic field of data science.


Enroll now to embark on a transformative learning journey and become a proficient data scientist, mastering the entire spectrum of the data science life cycle with confidence and finesse.

Who this course is for:

  • The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills in Data Science
  • People interested to learn data science using Python
  • You should take this course if you want to become a Data Scientist