Advance Python | Python For Datascience

A Python-Based Datascience Roadmap

What you'll learn

The course is designed to provide students with a strong foundation in advanced Python programming, data analysis, and machine learning.

Students will learn advanced programming concepts, including list comprehensions, file I/O operations, exception handling, and lot more advance python concepts.

Data manipulation and analysis using the NumPy and Pandas libraries, covering data cleaning, preprocessing, and transformation techniques.

Data visualization using Matplotlib, Seaborn, and Plotly for creating informative and visually appealing plots and charts.

Implementation and evaluation of various machine learning algorithms, such as supervised and unsupervised learning, using the Scikit-learn library.

Optional exploration of advanced topics like natural language processing, web scraping, time series analysis, and recommender systems for a more comprehensive u

Requirements

Students should have understanding of fundamental Python concepts, including variables, data types, loops, and functions.

A genuine interest in working with data, conducting data analysis, and implementing machine learning models is crucial to fully benefit from the course content.

A foundational knowledge of basic mathematical concepts, such as algebra and statistics, will be helpful for comprehending certain aspects of data analysis, machine learning, and numerical computing.

Description

Ready to advance your Python skills? Our easy-to-follow Advanced Python course is tailored for learners of all levels, This course is crafted for students aspiring to master Python and dedicated to pursuing careers as data analysts or data scientists. It comprehensively covers advanced Python concepts, providing students with a strong foundation in programming and data analysis, focusing on data analysis, visualization, and machine learning. Discover the power of Python in handling complex data, creating engaging visuals, and building intelligent machine-learning models.Course Curriculum: –Introduction to PythonPython syntax and basic programming conceptsVariables, data types, and operatorsControl flow (conditionals and loops)Functions and modulesAdvanced Python ConceptsList comprehensions and generatorsFile I/O operationsException handlingObject-oriented programming (classes, objects, inheritance)Decorators and metaclassesNumPy (expand on the basic library coverage)Arrays and array operationsArray indexing and slicingBroadcasting and vectorizationMathematical functions and linear algebraArray manipulation and reshapingPandas (expand on the basic library coverage)Series and DataFrame data structuresData cleaning and preprocessing techniquesData manipulation and transformationHandling missing data and outliersMerging, joining, and reshaping datasetsData VisualizationAdvanced Matplotlib techniquesSeaborn for statistical data visualizationPlotly and interactive visualizationsCustomizing plots and aestheticsVisualizing geospatial dataMachine Learning with Scikit-learn (expand on the basic library coverage)Supervised learning algorithms (linear regression, logistic regression, support vector machines, decision trees, random forests, etc.)Unsupervised learning algorithms (clustering, dimensionality reduction)Model evaluation and validation techniquesHyperparameter tuning and model selectionFeature selection and feature engineeringDeep Learning with TensorFlow or PyTorch (optional, if time permits)Introduction to neural networks and deep learningBuilding and training neural networksConvolutional neural networks for image classificationRecurrent neural networks for sequence dataTransfer learning and pre-trained modelsAdditional Topics (optional, based on available time and student interests)Natural Language Processing (NLP) with NLTK or SpaCyWeb scraping and data collectionTime series analysis and forecastingRecommender systemsIntroduction to Big Data and distributed computing with PySparkCase Studies and ProjectsApply the learned concepts and libraries to real-world datasetsWork on data science projects with varying complexitiesPractice problem-solving and critical thinkingWith hands-on practice and expert guidance, you'll be prepared for rewarding opportunities in data science and analytics. **   Join us now to become a proficient Python data analyst and unlock a world of possibilities!   **

Overview

Section 1: Introduction to Python

Lecture 1 Introduction to python (part-1)

Lecture 2 Introduction to python (part-2)

Section 2: Advanced Python Concepts

Lecture 3 List Comprehension and Generators

Lecture 4 File Handling

Lecture 5 Exception handling

Lecture 6 Object Oriented Programming (oops)

Lecture 7 Decorators and Metaclasses

Section 3: NumPy (expand on the basic library coverage)

Lecture 8 Arrays and Arrays Operations

Lecture 9 Array Indexing and Slicing

Lecture 10 Broadcasting and Vectorization

Lecture 11 Mathematical functions and Linalg

Lecture 12 Array Manipulation and Reshaping

Section 4: Pandas (expand on the basic library coverage)

Lecture 13 Pandas Datastructure

Lecture 14 Data Transformation and Manipulation

Lecture 15 Data Cleaning and Preprocessing

Lecture 16 Joining Merging and Reshaping

Section 5: Data Visualization

Lecture 17 Advanced Matplolib Techniques

Lecture 18 Seaborn

Lecture 19 Plotly

Lecture 20 Geospatial_Data_Analysis

Section 6: Supervised Learning Algorithms

Lecture 21 Linear Regression

Lecture 22 Logistic Regression

Lecture 23 Support Vector Machines, Decision Trees, Random Forests,

Section 7: Unsupervised Learning Algorithms

Lecture 24 24. Unsupervised Learning

Students interested in exploring data analysis, cleaning, and preprocessing techniques using Python will find this course helpful in understanding how to work w,For students keen on expanding their knowledge beyond basic programming, this course delves into advanced Python concepts, object-oriented programming, and more,Students those who wants to learn advanced Python concepts, and are intrested towards the field of datascience,Students and professionals in the field of machine learning and artificial intelligence looking to strengthen their understanding of Python for implementing and,Data analysts and data scientists seeking to leverage Python for advanced data manipulation, analysis, and visualization tasks.,Intermediate Python developers aiming to enhance their skills and delve deeper into advanced programming concepts.,Software engineers interested in expanding their knowledge of Python for various applications, including web development, data processing, and automation.