Online self-learning courses offer autonomy, allowing individuals to learn at their own pace. They provide structured training materials with review exercises to enhance understanding. Utilizing multimedia resources like videos and presentations, learners actively engage with the content. while flexibility enables customization of study schedules. This fosters an environment conducive to effective learning and skill development, accommodating personal commitments.
GoLogica Data Science with Python course assists participants in gaining the skills and knowledge necessary for success in data science. The online Training course teaches participants Python programming for data analysis, machine learning, and visualization. This ensures that participants can easily apply these skills in real-life situations for practical purposes.
The class can be found on the internet, so anyone anywhere in the world can try it. You only need a computer and internet to begin learning data science with Python. Sign up today and start a trip to learn these important skills in an easy-to-reach learning place.
GoLogica uses a simple and useful way to teach the Python Data Science online course. Our instructors, who know a lot about this field, help people understand both the ideas they need to learn and how to use them in real life. The program is made to help students learn Python skills, manage data well, do math studies and machine learning methods. They will also understand how to show information clearly in a good way.
GoLogica Applied Data Science with Python training helps you:
Use your skills on real-life tasks, and gain hands-on experience.
Get a certificate when you finish, showing that you know about Data Science.
Get better job chances by learning popular data science skills.
Welcome to GoLogica, your opportunity to Learn Data Science with Python. In a time where speed and quality are highest, our complete training program is planned to provide you with the skills and knowledge needed to increase in a Data science environment. At GoLogica, we employ an active teaching approach. Our expert instructors combine theoretical knowledge with practical exercises and real-world case studies. Live projects and interactive sessions make sure that you get a deep understanding of Data science.
Features of Data Science with Python:
Open Source: Python is a free language, which means anyone can use it. This helps the data science community work together on different projects
Data Visualization: Matplotlib, Seaborn, and Plotly are strong tools for making pictures and charts that help us understand data better
Big Data Processing: Programs like PySpark help us handle and work with very large amounts of data.
Salary Trends:
According to Glassdoor, The average salary of an Applied Data Science with Python professional typically ranges from $74k to $82k PA. It’s depending on factors such as experience, location, and specific job responsibilities.
Want To Learn More?
Applied Data Science with Python Curriculum
Python Data Science-Centric Libraries
NumPy
NumPy Arrays
Select NumPy Operations
SciPy
pandas
Creating a pandas DataFrame
Fetching and Sorting Data
Scikit-learn
Matplotlib
Seaborn
Python Dev Tools and REPLs
IPython
Jupyter
Jupyter Operation Modes
Jupyter Common Commands
Anaconda
What is Data Science?
Data Science, Machine Learning, AI?
The Data-Related Roles
The Data Science Ecosystem
Tools of the Trade
Who is a Data Scientist?
Data Scientists at Work
Examples of Data Science Projects
An Example of a Data Product
Applied Data Science at Google
Data Science Gotchas
Typical Data Processing Pipeline
Data Discovery Phase
Data Harvesting Phase
Data Priming Phase
Exploratory Data Analysis
Model Planning Phase
Model Building Phase
Communicating the Results
Production Roll-out
Data Logistics and Data Governance
Data Processing Workflow Engines
Apache Airflow
Data Lineage and Provenance
Apache NiFi
Descriptive Statistics
Non-uniformity of a Probability Distribution
Using NumPy for Calculating Descriptive Statistics Measures
Finding Min and Max in NumPy
Using pandas for Calculating Descriptive Statistics Measures
Correlation
Regression and Correlation
Covariance
Getting Pairwise Correlation and Covariance Measures
Finding Min and Max in pandas DataFrame
Repairing and Normalizing Data
Dealing with the Missing Data
Sample Data Set
Getting Info on Null Data
Dropping a Column
Interpolating Missing Data in pandas
Replacing the Missing Values with the Mean Value
Scaling (Normalizing) the Data
Data Preprocessing with scikit-learn
Scaling with the scale() Function
The MinMaxScaler Object
Data Visualization
Data Visualization in Python
Matplotlib
Getting Started with matplotlib
The matplotlib.pyplot.plot() Function
The matplotlib.pyplot.bar() Function
The matplotlib.pyplot.pie () Function
Subplots
Using the matplotlib.gridspec.GridSpec Object
The matplotlib.pyplot.subplot() Function
Figures
Saving Figures to a File
Seaborn
Getting Started with seaborn
Histograms and KDE
Plotting Bivariate Distributions
Scatter plots in seaborn
Pair plots in seaborn
Heatmaps
ggplot
Types of Machine Learning
Terminology: Features and Observations
Representing Observations
Terminology: Labels
Terminology: Continuous and Categorical Features
Continuous Features
Categorical Features
Common Distance Metrics
The Euclidean Distance
What is a Model
Supervised vs Unsupervised Machine Learning
Supervised Machine Learning Algorithms
Unsupervised Machine Learning Algorithms
Choosing the Right Algorithm
The scikit-learn Package
scikit-learn Estimators, Models, and Predictors
Model Evaluation
The Error Rate
Confusion Matrix
The Binary Classification Confusion Matrix
Multi-class Classification Confusion Matrix Example
ROC Curve
Example of an ROC Curve
The AUC Metric
Feature Engineering
Scaling of the Features
Feature Blending (Creating Synthetic Features)
The 'One-Hot' Encoding Scheme
Example of 'One-Hot' Encoding Scheme
Bias-Variance (UnderfittingvsOverfitting) Trade-off
The Modeling Error Factors
One Way to Visualize Bias and Variance
UnderfittingvsOverfitting Visualization
Balancing Off the Bias-Variance Ratio
Regularization in scikit-learn
Regularization, Take Two
Dimensionality Reduction
PCA and isomap
The Advantages of Dimensionality Reduction
The LIBSVM format
Life-cycles of Machine Learning Development
Data Splitting into Training and Test Datasets
ML Model Tuning Visually
Data Splitting in scikit-learn
Cross-Validation Technique
Hands-on Exercise
Classification (Supervised ML) Examples
Classifying with k-Nearest Neighbors
k-Nearest Neighbors Algorithm
Hands-on Exercise
Regression Analysis
Regression vs Correlation
Regression vs Classification
Simple Linear Regression Model
Linear Regression Illustration
Least-Squares Method (LSM)
Gradient Descent Optimization
Multiple Regression Analysis
Evaluating Regression Model Accuracy
The R2 Model Score
The MSE Model Score
Logistic Regression (Logit)
Interpreting Logistic Regression Results
Decision Trees and Terminology
Properties of Decision Trees
Decision Tree Classification in the Context of Information Theory
The Simplified Decision Tree Algorithm
Using Decision Trees
Random Forests
Hands-On Exercise
Support Vector Machines (SVMs)
Naive Bayes Classifier (SL)
Naive Bayesian Probabilistic Model in a Nutshell
Bayes Formula
Classification of Documents with Naive Bayes
Unsupervised Learning Type: Clustering
Clustering Examples
k-Means Clustering (UL)
k-Means Clustering in a Nutshell
k-Means Characteristics
Global vs Local Minimum Explained
Hands-On Exercise
XGBoost
Gradient Boosting
Hands-On Exercise
A Better Algorithm or More Data?
What is Python?
Additional Documentation
Which version of Python am I running?
Python Dev Tools and REPLs
IPython
Jupyter
Jupyter Operation Modes
Jupyter Common Commands
Anaconda
Python Variables and Basic Syntax
Variable Scopes
PEP8
The Python Programs
Getting Help
Variable Types
Assigning Multiple Values to Multiple Variables
Null (None)
Strings
Finding Index of a Substring
String Splitting
Triple-Delimited String Literals
Raw String Literals
String Formatting and Interpolation
Boolean and Boolean Operators
Numbers
Looking Up the Runtime Type of a Variable
Divisions
Assignment-with-Operation
Comments:
Relational Operators
The if-elif-else Triad
An if-elif-else Example
Conditional Expressions (a.k.a. Ternary Operator)
The While-Break-Continue Triad
The for Loop
try-except-finally
Lists
Main List Methods
Dictionaries
Working with Dictionaries
Sets
Common Set Operations
Set Operations Examples
Finding Unique Elements in a List
Enumerate
Tuples
Unpacking Tuples
Functions
Dealing with Arbitrary Number of Parameters
Keyword Function Parameters
The range Object
Random Numbers
Python Modules
Importing Modules
Installing Modules
Listing Methods in a Module
Creating Your Own Modules
Creating a Runnable Application
List Comprehension
Zipping Lists
Working with Files
Reading and Writing Files
Reading Command-Line Parameters
Accessing Environment Variables
What is Functional Programming (FP)?
Terminology: Higher-Order Functions
Lambda Functions in Python
Example: Lambdas in the Sorted Function
Other Examples of Using Lambdas
Regular Expressions
Using Regular Expressions Examples
Python Data Science-Centric Libraries
The GoLogica certification is widely acknowledged, enhancing the credibility of your resume and opening doors to high-level positions in leading multinational corporations globally.
At the end of this course, you will receive a course completion certificate which certifies that you have successfully completed GoLogica training in Applied Data Science with Python technology.
You will get certified in Applied Data Science with Python by clearing the online examination with a minimum score of 70%.
Get Certification
Applied Data Science with Python Objectives
This skills based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn and nltk.
Data science has a broader focus and encompasses the entire data analysis and modeling process, including data exploration, cleaning, and algorithm development. Applied data science narrows the focus to applying data science techniques to solve specific problems in a particular domain or industry.
It is used for general-purpose programming, but it has also become popular in the field of Data Science because of its ease of use and flexibility. Python libraries are tools that extend the functionality of Python and make it easier to perform specific tasks such as data manipulation or machine learning.
Data preprocessing involves cleaning, transforming, and organizing raw data into a usable format for analysis. It includes tasks such as handling missing values, removing duplicates, and scaling features. It's crucial because the quality of analysis and machine learning models heavily depends on the quality of data.
Supervised learning involves training a model on a labeled dataset, where the algorithm learns to map input data to the correct output based on example input-output pairs. Unsupervised learning, on the other hand, deals with unlabeled data and the model aims to find patterns or structures in the data without explicit guidance.
Common techniques for feature selection include univariate feature selection, which selects the best features based on univariate statistical tests; recursive feature elimination, which recursively removes the least important features and selects the best subset; and feature importance using ensemble methods like Random Forests or Gradient Boosting.
NumPy and Pandas are essential libraries in Python for data manipulation and analysis. NumPy provides support for numerical operations and multidimensional arrays, while Pandas offers data structures like DataFrames that make it easy to work with structured data. These libraries enable efficient data manipulation, computation, and analysis, forming the backbone of many data science workflows.
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Visit GoLogica website, locate the 'Certificates' section, follow the instructions to verify your course completion by completing the exam and Get more than 70% marks. And download your certificate.
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GoLogica have a 10+ year’s good track record in the training market. However, it was founded in 2013.
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Self-paced training allows learners to study at their own speed, while Live Online training offers real-time, interactive sessions with an instructor.
Self-paced learning offers flexibility, personalized progress, and the ability to review materials at your own convenience.
Live online training offers real-time interaction, immediate feedback, and networking opportunities, which self-paced learning lacks.
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Applied Data Science with Python rated
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Zamir
Gologica’s trainer, top-rated by customers, delivered exceptionally useful information in Applied Data Science with Python Training, enhancing my skills significantly. Thanks to Gologica for this valuable learning experience!
