Data Science with Pyhton Certification - eLearning
450,00 EUR
- 50 hours
The Python for Data Science course covers the fundamental programming concepts with Python and explains data analytics, machine learning, data visualization, web scraping, and natural language processing. You will gain a comprehensive understanding of the various packages and libraries required to perform the data analysis aspects.
Key Features
Language
Course and material are in english
Level
Beginner - intermediate level
Access
1 year access to the self-paced study eLearning platform 24/7
6 hours of video content
with 40 hours recommended study time & practices
Practices
Virtual labs, Test simulation, End-Projects
No Exam
No exam for the course but student will get certification of training completion
Learning Outcomes
At the end of this Data Science with Python eLearning Course, you will be able to:
Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing.
Install the required Python environment and other auxiliary tools and libraries.
Understand the essential concepts of Python programming, such as data types, tuples, lists, basic operators, and functions.
Perform high-level mathematical computing using the NumPy package and its extensive library of mathematical functions.
Perform high-level mathematical computing using the NumPy package and its extensive library of mathematical functions.
Perform scientific and technical computing using the SciPy package and its sub-packages, such as Integrate, Optimise, Statistics, IO, and Weave.
Execute data analysis and manipulation using data structures and tools provided in the Pandas package.
Gain expertise in machine learning using the Scikit-Learn package
Understand supervised and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN, and pipeline.
Use the Scikit-Learn package for natural language processing.
Use the matplotlib library of Python for data visualization
Extract valuable data from websites by performing web scrapping using Python
Integrate Python with Hadoop, and MapReduce
Course Content
Introduction to Data Science
Lesson 01
- Data Science and its Applications
- The Data Science Process: Part 1
- The Data Science Process: Part 2
Essentials of Python Programming
Lesson 02
- Setting Up Jupyter Notebook
- Python Functions
- Python Types and Sequences
- Python Strings Deep Dive
- Python Demo: Reading and Writing csv files
- Date and Time in Python
- Objects in Python Map
- Lambda and List Comprehension
- Why Python for Data Analysis?
- Python Packages for Data Science
- StatsModels Package
- Scipy Package
NumPy
Lesson 03
- Fundamentals of NumPy
- Array shapes and axes in NumPy: Part A
- NumPy Array Shapes and Axes: Part B
- Arithmetic Operations
- Conditional Logic
- Common Mathematical and Statistical Functions in Numpy
- Indexing And Slicing
- File Handling
Linear Algebra
Lesson 03
- Introduction to Linear Algebra
- Scalars and Vectors
- Dot Product of Two Vectors
- Linear independence of Vectors
- Norm of a Vector
- Matrix operations
- Rank of a Matrix
- Determinant of a matrix and Identity matrix or operator
- Inverse of a matrix and Eigenvalues and Eigenvectors
- Calculus in Linear Algebra
Statistic Fundamentals
Lesson 05
- Importance of Statistics with Respect to Data Science
- Common Statistical Terms
- Types of Statistics
- Data Categorization and Types
- Levels of Measurement
- Measures of Central Tendency
- Measures of Dispersion
- Random Variables
- Sets
- Measures of Shape (Skewness & Kurtosis)
- Covariance and Correlation
Probability Distribution
Lesson 06
- Probability,its Importance, and Probability Distribution
- Probability Distribution : Binomial Distribution
- Probability Distribution: Poisson Distribution
- Probability Distribution: Normal Distribution
- robability Distribution: Bernoulli Distribution
- Probability Density Function and Mass Function
- Cumulative Distribution Function
- Central Limit Theorem
- Estimation Theory
Advanced Statistics
Lesson 07
- Distribution
- Kurtosis Skewness and Student's T-distribution
- Hypothesis Testing and Mechanism
- Hypothesis Testing Outcomes: Type I and II Errors
- Null Hypothesis and Alternate Hypothesis
- Confidence Intervals
- Margins of error
- Comparing and Contrasting T test and Z test
- Bayes Theorem
- Chi Sqare Distribution
- Chi Square Test and Goodness of Fit
- Analysis of Variance or ANOVA
- ANOVA Termonologies
- Partition of Variance using Python
- F - Distribution using Python
- F - Test
Pandas
Lesson 08
- Pandas Series
- Querying a Series
- Pandas Dataframes
- Pandas Panel
- Common Functions In Pandas
- Pandas Functions Data Statistical Function, Windows Function
- Pandas Function Data and Timedelta
- Categorical Data
- Working with Text Data
- Iteration
- Sorting
- Plotting with Pandas
Data Analysis
Lesson 09
- Understanding Data
- Types of Data Structured Unstructured Messy etc
- Working with Data Choosing appropriate tools, Data collection, Data wrangling
- Importing and Exporting Data in Python
- Regular Expressions in Python
- Manipulating text with Regular Expressions
- Accessing databases in Python
Data Wrangling
Lesson 10
- Pandorable or Idiomatic Pandas Code
- Loading Indexing and Reindexing
- Merging
- Memory Optimization in Python
- Data Pre Processing: Data Loading and Dropping Null Values
- Data Pre-processing Filling Null Values
- Data Binning Formatting and Normalization
- Data Binning Standardization
- Describing Data
Data Visualization
Lesson 11
Principles of information visualization
Visualizing Data using Pivot Tables
Data Visualization Libraries in Python Matplotlib
Graph Types
Data Visualization Libraries in Python Seaborn, Ploty, Bokeh
Using Matplotlib to Plot Graphs
Plotting 3D Graphs for Multiple Columns using Matplotlib
Using Matplotlib with other python packages
Using Seaborn to Plot Graphs
Plotting 3D Graphs for Multiple Columns Using Seaborn
Introduction to Plotly and Bokeh
Who Should Enroll in this Program?
This course is ideal for individuals who are interested in pursuing a career in data science, machine learning, or artificial intelligence, and are looking to enhance their Python programming and data analysis skills.
Aspiring Data Scientists
Data Analysts
Software Engineers or Programmers
Researchers and Academics
Machine Learning Enthusiasts
Students and Graduates
Prerequisites
Learners need to possess an undergraduate degree or a high school diploma. Additionally, a curiosity for data analysis and a desire to explore the applications of Python in the field of data science is highly encouraged. It is also recommended to have:
- Basic Python Programming Knowledge: Familiarity with basic Python programming concepts such as variables, loops, functions, and control flow.
- Basic Understanding of Statistics: A basic understanding of statistics, including concepts like mean, median, standard deviation, probability, and correlation.
- Mathematics: Basic math skills, particularly in areas like algebra and linear algebra, will be helpful, especially when working with machine learning algorithms or models.
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