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Data Science With Python PLUS Deep Learning & PostgreSQL
Rating: 3.7 out of 5(10 ratings)
471 students

Data Science With Python PLUS Deep Learning & PostgreSQL

Learn tools, techniques, careers, companies, k-means, clustering, deep learning, neural networks, machine learning ++
Created byPeter Alkema, Quentin Ballard-Tremeer
Last updated 11/2022
English
English [Auto],

What you'll learn

  • Data Science and Its Types
  • Top 10 Jobs in Data Science
  • Tools of Data Science
  • Variables and Data in Python
  • Introduction to Python
  • Probability and Statistics
  • Functions in Python
  • Operator in Python
  • DataFrame with Excel
  • Dictionaries in Python
  • Tuples and loops
  • Conditional Statement in Python
  • Sequences in Python
  • Iterations in Python
  • Multiple Regression in Python
  • Linear Regression
  • Libraries in Python
  • Numpy and SK Learn
  • Pandas in Python
  • K-Means Clustering
  • Clustering of Data
  • Data Visualization with Matplotlib
  • Data Preprocessing in Python
  • Mathematics in Python
  • Data Visualization with Plotly
  • What is Deep Learning?
  • Deep Learning
  • Neural Network
  • Tensor Flow
  • PostgreSQL
  • Machine Learning and Data Science
  • Machine Learning Models
  • Data Science Projects: Real World Problems

Explore related topics

Course content

22 sections • 296 lectures • 29h 35m total length

  • Introduce Yourself To Your Fellow Students And Tell Everyone What Are Your Goals0:17

    This is your opportunity to share something about yourself with the rest of the students in this course. Tell us all about your goals and what you want to achieve. You can come back to this board and add more thoughts as you go through the course and achieve your goals. Seeing all the other students in the course will also motivate you and keep you going as you participate in this community of learning.

  • Let's Celebrate Your Progress In This Course: 25% > 50% > 75% > 100%!!1:20

    Throughout this course, we will celebrate your progress at 25%, 50%, 75%, and 100%. I really want you to succeed but you need to take action and keep going so look forward to these milestones of progress. I will see you there and cheer you on as you keep going from one milestone to the next >>

  • What is Data Science?1:38

    Define data science as an interdisciplinary field that uses the scientific method, processes, algorithms, and systems to extract knowledge from noisy, structured, and unstructured data and apply it across domains.

  • Application of Data Science9:00

    Explore the diverse applications of data science across health care, gaming, image recognition, recommendation systems, fraud detection, logistics, internet search, and speech recognition.

  • Types of Data Science2:37

    Explore the types of data science, highlighting business intelligence and how strategies and technologies drive data analysis and management of business information.

  • Cloud Computing2:54

    Provide on-demand access to data storage and computing power without direct active management by the user. Distribute functions across multiple data centers in different locations.

  • Cyber Security4:19

    Explore cybersecurity analysis as a key data science practice that protects a company network and systems from cyber attacks, using monitoring, contingency planning, and risk alerts.

  • Data Engineering1:06

    Data engineering builds systems to collect and use data, enabling subsequent analysis, data science, and often machine learning.

  • Data Mining2:40
  • Data Visualization1:34

    Explore data visualization as an essential, interdisciplinary method in data science that uses graphic representation of data to communicate complex information, including time series data.

  • Data Warehousing2:15
  • Machine Learning2:32

    Explore how machine learning, a core data science and artificial intelligence field, builds methods that learn from data to improve performance on various tasks, and its relation to deep learning.

  • Math and Stats in Data Science5:14

    Explore how mathematics and statistics underpin data science, and outline three levels of data literacy: recognizing possibilities, applying techniques like clustering, and selecting effective methods using Python or R.

  • Database Programming2:57
  • Database Programming 22:13

    Clean data and optimize it for maintainable code, while software developers write the code; retrieve data from databases and perform open-ended analysis, manipulate data, summarise, visualise, and share data.

  • Business Understanding2:10

Requirements

  • Internet connection
  • PC, Laptop, Mobile
  • No prior knowledge is required. We shall start from basics and finish on a pro level.

Description

Get instant access to a workbook on Data Science, follow along, and keep for reference

Introduce yourself to our community of students in this course and tell us your goals with data science

Encouragement and celebration of your progress every step of the way: 25% > 50% > 75% & 100%

30 hours of clear and concise step-by-step instructions, lessons, and engagement

This 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, Machine Learning, 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:

  • Data Science and Its Types

  • Top 10 Jobs in Data Science

  • Tools of Data Science

  • Variables and Data in Python

  • Introduction to Python

  • Probability and Statistics

  • Functions in Python

  • Operator in Python

  • DataFrame with Excel

  • Dictionaries in Python

  • Tuples and loops

  • Conditional Statement in Python

  • Sequences in Python

  • Iterations in Python

  • Multiple Regression in Python

  • Linear Regression

  • Libraries in Python

  • Numpy and SK Learn

  • Pandas in Python

  • K-Means Clustering

  • Clustering of Data

  • Data Visualization with Matplotlib

  • Data Preprocessing in Python

  • Mathematics in Python

  • Data Visualization with Plotly

  • What is Deep Learning?

  • Deep Learning

  • Neural Network

  • Tensor Flow

  • PostgreSQL

  • Machine Learning and Data Science

  • Machine Learning Models

  • Data Science Projects: Real World Problems

  • ...and more!

Contents and Overview

You'll start with What is Data Science?; Application of Data Science; Types of Data Science; Cloud Computing; Cyber Security; Data Engineering; Data Mining; Data Visualization; Data Warehousing; Machine Learning; Math and Stats in Data Science; Database Programming; Database Programming 2; Business Understanding; Data Science Companies; Data Science Companies 2; Top 10 Jobs and Skills in Data Science; Top 10 Jobs and Skills in Data Science 2; Tools and Techniques in Data Science; Tools and Techniques in Data Science; Interview 1; Interview 2; Statistics Coding File; Median in Statistics; Finding Mean in Python; fMean in Statistics Low and High Mean in Statistics; Mode in Statistics; pVariance in Statistics; Variance and Co-variance in Statistics; Quantiles and Normal Distribution in Statistics; Statistics 9; Coding File; Excel: Creating a Row; Creating and Copying Path of an Excel Sheet; Creating and Copying Path of an Excel Sheet 2; Importing Data Set in Python from Excel; Coding File; Linear  Regression; Linear Regression Assignment Code; Linear Regression Assignment Code; NumPy and SK Learn Coding File; Numpy: Printing an Array; Numpy: Printing Multiple Array; Numpy: dtypes Parameters; Numpy: Creating Variables; Numpy: Boolean Using Numpy; Numpy: Item Size Using Bit Integers; Shape and Dimension Using Numpy; Numpy for 2D and 3D Shapes; Arrangement of Numbers Using NumPy; Types of Numbers Using NumPy; Arrangement of Random Numbers Using NumPy; NumPy and SciPy; Strings in Using NumPy; Numpy: dtype bit integers; Inverse and Determinant Using SciPy; Spec and Noise; Interpolation Using SciPy; Optimization Using SciPy; Defining Trigonometric Function; NumPy Array.

We will also cover Pandas Coding File; What is Pandas?; Printing Selected Series Using Pandas; Printing Pandas Series; Pandas Selected Series; DataFrame in Pandas; Pandas Data Series 2; Pandas Data Series 3; Pandas Data Series 0 and 1; Pandas for Sets; Pandas for Lists and Items; Pandas Series; Pandas Dictionaries and Indexing; Pandas for Boolean; Pandas iloc; Random State Series in Pandas; DataFrame Columns in Pandas; Size and Fill in Pandas; Loading Data Set in Pandas; Google Searching csv File; Visualization of Excel Data in Pandas; Visualization of Excel Data in Pandas 2; Excel csv File in Pandas; Loading and Visualization of Excel Data in Pandas 3; Histogram Using Pandas; Percentile in Pandas; What is Clustering and K-Mean Clustering?; Python Coding File; Simple Plotting; Simple Plotting 2; Scatter Plotting; Marker Point Plotting; Assignment Code; Error bar Plotting; Error bar Color Plotting; Gaussian Process Code; Error in Gaussian Process Code; Histo Plotting; Histo Plotting 2; Color bar Plotting; Legend Subplots; Trigonometry Plotting; Color bar Plotting 2; Trigonometry Plotting 2; Subplots with Font Size; Subplots with Font Size 2; Plotting Points of Subplots; Grid Plotting; Formatter Plotting Coding; Grid and Legend Code Plotting; Color Code Coding;Histogram Color Code Coding; Histo and Line Plotting; Color Scheme for Histo; 3D Plotting; 3D Trigonometry Plotting; 3D Color Scheme; Neural Network Coding File; Neural  Network Model for Supervised Learning; MLPClassifier Neural Network; Neural  Prediction and Shape.

This course will also tackle  Coding File; Addition in Tensor; Multiplication in Tensor; Tensor of Rank 1; Tensor  for Boolean and String; Print 2 by 2 Matrix; Tensor  Shape; Square root Using Tensor; Variable in Tensor; Assignment; What is PostgreSQL?; Coding File; Naive Bayes Model of Machine Learning; Scatter Plotting of Naive Bayes Model; Model Prediction; Fetching Targeting Data; Extracting Text Using Naive Bayes Model; Ifidvectorizer for Multinomial; Defining Predict Category; Coding File; Iris Seaborn; Linear Regression Model; Adding and Subtraction in Python; Adding and Subtraction 2; Variable Intersection in Python; Finding len in Python; Basic Math in Python; Basic Math in Python 2; Basic Math in Python 3; Basic Math in Python 4; Trigonometry in Python; Degree and Radian in Python; Finding Difference Using Variables; Intersection of Sets in Python; Difference of Sets in Python; issuperset Code in Python; issuperset Code 2; Boolean Disjoint in Python; Variables in Python; Coding File; Current Date Time in Python; dir Date Time; Time Stamp in Python; Printing Day, Month and Year; Printing Minutes and Seconds; Time Stamp of Date and minutes; Microsecond in Python; Date Time Template; Time Stamp 2; Time Stamp 3; Time difference in Python; Time Difference in Python 2; Time Delta; Time Delta 2; Union of Sets; Time Delta 3; Assignment Code for Date and Time 1; Assignment Code for Date and Time 2; Assignment Code for Date and Time 3; Symmetric Difference in Python; Bitwise operator in Python; Logical Reasoning in Python; Bin Operator; Bin Coding; Binary Coding 2; Boolean Coding; Del Operator; Hello World; Boolean Algebra; Printing Array; Printing Array 2; Append Array; Insertion in an Array; Extension in an Array; Remove an Array; Indexing an Array in Python; Reverse and Buffering an Array in Python; Array into String; char Array in Python; Formatting an Array; Printing List; Printing Tuples in Python; Easy Coding; Printing a String; Printing Selected Strings; Printing New Line; Assigning Code; Open a File in Python; Finding a Path in Python File; Printing a String in Python 2; Printing Multiple String in Python; Addition and Multiplying String in Python; Boolean in String; Selection of Alphabets in String; Choosing Specific Words from Code; Choosing Words 2; Combining Integers and Strings; Assigning Values to String; String and Float; ord and chr Coding in Python; Binary Operation Code in Python; Binary Operation Code 2; int into Decimal in Python; Decimal to Binary; Adding Lists in Python; Empty List; Matrix Operation in Python; Dictionaries and Lists; Del Operator in Python; Printing Date Time in Python; Dictionaries Items; Pop Coding in Python; Lists and Dictionaries; Matrix Coding; mel Coding; mel Coding; Dictionaries Key; Finding Square of Lists; Dictionary Coding; Print Selected Lists; Tuples in Python; Tuples Coding; Print Tuples in Python; Sorted Tuples in Python; Add List in Tuples; Index in Tuples; List and Tuples in Python; Open My File; Scan Text File; Assignment; Lists and Dictionaries; Read Lines in Python.

Then, Linear Functions; Inner Product in Python; Taylor's Approximation in Python; Regression Model; Norm Using Python; Cheb Bound in Python; Zeroes and One in NumPy; Linear Combination of Vectors; Vectors and Scalars in Python; Inner Product of Vectors; Difference and Product in Python; Finding Angle in Python; Product of Two Vector in Python; Convolution in Python; Finding Norm in Python; Sum and Absolute in Python; Vstack and Hstack in Python; Derivatives Using SymPy; Difference Using SymPy; Partial Derivatives Using SymPy; Integration Using SymPy; Integration Using SymPy; Limit Using SymPy; Series in Python; Printing Leap Year in Python; Year Format in Python; Pyaudio in Python; Pyaudio in Python 2; Pyaudio in Python 3; Pyaudio in Python 4; Read Frame in Python; Shelve Library in Python; Assignment Code; Pandas Data Frame; Assignment Code.

We can't wait to see you on the course!

Enrol now, and we'll help you improve your data science skills!

Peter

Who this course is for:

  • Those who want to have career in data science.
  • Those who have interest in data science and want to apply their knowledge in their field or profession.
  • Those who want to learn the application of data science using python.

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