Data Science & Python: Maths, Python Libraries, Statistics

Learn Python, Numpy, Pandas, Matplotlib, Linear Regression, Algebra, Statistics, Calculus, Projects & Data Visualisation

What you'll learn
What is Python
Uses of Python
How to write code in Python
What are Python libraries
What is Anaconda
What is Jupyter Notebook
What is Numpy
What is Matplotllib
How to plot in Matplotlib
What is Scipy
What is Scikit
What is Pandas
How to import files in Jypyter notebook using Pandas
How to create files using Pandas
Basic math in Python
Linear Algebra in Pythom
Statistics in Python
2d and 3d plotting in Python
Linear regression in Python
differential and Integral calculus in Python
Requirements
Internet connection
Laptop or PC or Mobile Phone
Motivation towards new learning
Description
Get instant access to a 73-page workbook on Data Science, follow along, and keep for referenceIntroduce yourself to our community of students in this course and tell us your goals with data scienceEncouragement and celebration of your progress every step of the way: 25% > 50% > 75% & 100%Over 13 hours of clear and concise step-by-step instructions, lessons, and engagementThis data science course provides participants with the knowledge, skills, and experience associated with Data Science. Students will explore a range of data science tools, algorithms, linear programming and statistical techniques, with the aim of discovering hidden insights and patterns from raw data in order to inform scientific business decision-making.What  you will learn:Introduction to Python; what is Python, Anaconda, libraries, Numpy, Matplotlib, SciPy and SciKit LearnLearn mathematics by coding in python; basic maths, variables. solutions of equations. logarithmic and exponential functions. polynomials, complex numbers and trigonometryStatistics by coding in PythonLinear Algebra for data science: matrices. determinants, inverse, solutions, scalars and vectorsDetailed introduction and demo of NumpyLinear algebra in Python as well as calculus. Matplotlib and moreLear Data Science projects in Pandas: importing files, creating data framesRegression analysis using SKLearnData science careers in a Q&A Webinar plus additional insights; learn from other students questionsWho are the Instructors?Dr. Allah Dittah is your lead instructor – a PhD and lecturer making a living from teaching Python, advanced mathematics and data science. As a data science expert, he has joined with content creator Peter Alkema to bring you this amazing new course.You'll get premium support and feedback to help you become more confident with data science!We can't wait to see you on the course!Enrol now, and we'll help you improve your data science skills!Peter and Allah

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Preview AND Download Your 73 Page Python & Data Science Workbook

Lecture 3 Introduce Yourself To Your Fellow Students And Tell Everyone What Are Your Goals

Lecture 4 Let's Celebrate Your Progress In This Course: 25% > 50% > 75% > 100%!!

Lecture 5 Download your project files: Pandas, NumPy & Matplotlib

Section 2: Introduction to Python

Lecture 6 What is Python

Lecture 7 Uses of Python

Lecture 8 What is Anaconda and its application

Lecture 9 What is a Jupyter Notebook and how to code in Jupyter notebook

Lecture 10 What is a Library

Lecture 11 What are Python Libraries

Lecture 12 What is Numpy

Lecture 13 What is Matplotlib

Lecture 14 What is Pandas

Lecture 15 What is SciPy Library in Python

Lecture 16 What is SciKit Learn

Section 3: Learn Mathematics by Coding in Python

Lecture 17 Basics Math in Python

Lecture 18 Variables in Python

Lecture 19 Solution of Quadratic and Linear Equations

Lecture 20 Logarithmic and Exponential Function in Python

Lecture 21 Logarithmic and Exponential Function in Python Lecture 2

Lecture 22 Polynomials in Python

Lecture 23 Complex Numbers in Python

Lecture 24 Trigonometry in Python

Lecture 25 You've Achieved 25% >> Let's Celebrate Your Progress And Keep Going To 50% >>

Section 4: Learn Statistics by Coding in Python

Lecture 26 Statistics in Python

Section 5: Linear Algebra for Data Science

Lecture 27 What is Matrix

Lecture 28 Rows and Columns in Matrix

Lecture 29 Order or Dimension of a Matrix

Lecture 30 Transpose of a Matrix

Lecture 31 Addition and Subtraction of Matrices

Lecture 32 Multiplication of Matrices

Lecture 33 Determinant of Matrices

Lecture 34 Inverse of a Matrix

Lecture 35 Solution of System of Linear Equations

Lecture 36 Scalars and Vectors

Lecture 37 Addition and Subtraction of Vectors

Section 6: Introduction to NumPy: 5 Different Code Projects

Lecture 38 Introduction to Numpy

Lecture 39 You've Achieved 50% >> Let's Celebrate Your Progress And Keep Going To 75% >>

Section 7: Linear Algebra in Python Using NumPy: 5 Projects

Lecture 40 Vectors Using Numpy

Lecture 41 Sum and Difference of Vectors

Lecture 42 Linear Algebra in Python Using Numpy Project 1

Lecture 43 Linear Algebra in Python Using NumPy Project 2

Lecture 44 Linear Algebra in Python Using NumPy Project 3

Lecture 45 Linear Algebra in Python Using NumPy Project 4

Lecture 46 Linear Combination in Python Using Numpy

Lecture 47 Inner Product in Python Using Numpy

Section 8: Calculus in Python Using Numpy

Lecture 48 Derivatives Using Numpy

Lecture 49 Integration in Python Using Numpy

Lecture 50 Limits Using Numpy

Lecture 51 You've Achieved 75% >> Let's Celebrate Your Progress And Keep Going To 100% >>

Section 9: Matplotlib

Lecture 52 Practical Example of Matplotlib

Lecture 53 Dot Plot in Matplotlib

Lecture 54 Simple Plot

Lecture 55 Plotting Linear, Quadratic, and Cubic Equations

Lecture 56 Labelling Using Matplotlib

Lecture 57 Random Plotting

Lecture 58 Random Plotting 2

Lecture 59 Scattering Plot in Matplotlib

Section 10: Data Science Projects in Pandas

Lecture 60 Import File in Pandas from Excel: Project 1

Lecture 61 Import File in Pandas from Excel: Project 2

Lecture 62 Creating DataFrame in Pandas: Project 3

Section 11: Regression Analysis: 2 Practice Exercise

Lecture 63 Linear Regression using sklearn

Lecture 64 You've Achieved 100% >> Let's Celebrate! Remember To Share Your Certificate!!

Section 12: Data Science Q&A Webinar & Insights: Learn Data Science Careers & More

Lecture 65 Introduction of the guest speaker and overview of the course

Lecture 66 Perspective on courses as one on data science and other courses

Lecture 67 Basic level of understanding about machines

Lecture 68 Pairing with physics and statistical major is good foundation for data science

Lecture 69 Having an overview on machine learning and the course

Lecture 70 Learn Statistics on data science

Lecture 71 Learn how could data science be part on marketing

Lecture 72 Which do you find more comfortable for automation, Phython or UiPath

Lecture 73 Thoughts and overview on the Python course

Lecture 74 Can data science help predict the stock price?

Lecture 75 Can phyton be used to sort through the data

Lecture 76 How does statistics relate to data science and it is used in business

Lecture 77 Game theory that are involved, and its application to the field of data scienc

Lecture 78 Education and games thoughts on the course

Section 13: [Optional] Full Length Data Science Q&A Webinar: Careers, Industry Insights ++

Lecture 79 [Optional] Full Length Data Science Q&A Webinar: Careers, Industry Insights ++

For those who love with learning,Data scientists,For those who want to apply Python in a practical way in their organization