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Upcoming Instructor-Led Applied Data Science With Python Class Date & Time as on October 06, 2025

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Applied Data Science With Python - Proximsoft

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About Applied Data Science With Python training

About Our Applied Data Science with Python
Our Applied Data Science with Python course is designed to provide comprehensive knowledge of essential data science methods and techniques. Through this course, you will learn how to use the Python language to clean, analyze, and visualize data effectively. We emphasize the application of statistical analysis and practical skills like data wrangling in Python to prepare you for real-world data challenges. The course also covers core Python libraries such as Pandas, NumPy, and Matplotlib to help you understand and manage data more efficiently. By the end of the course, you will gain hands-on experience in solving complex data problems using Python.
Objective of Our Applied Data Science with Python
This course aims to equip students with the necessary skills to succeed in data science. By the end of the course, you will be able to apply data science methods and techniques to real-world problems. We aim to help you acquire analysis skills through hands-on projects and practical exercises. You will become proficient in using NumPy and Pandas for data analysis and will understand the principles behind Applied Text Mining in Python. Our course is designed to make you an expert in using data visualization tools and techniques. Ultimately, the course will empower you with the skills needed to apply Python programming in various data science domains.
What You Will Learn in This Course
1. Data Analysis with Python using Pandas and NumPy.
2. Techniques for Data Wrangling in Python for better data preparation.
3. Implementing Applied Machine Learning in Python using scikit-learn.
4. Mastering Applied Plotting, Charting & Data Representation in Python with Matplotlib.
5. Fundamentals of Text Mining and Applied Social Network Analysis in Python.
6. Gaining a strong foundation in statistics and probability for data analysis.
Career opportunities
• Python Developer
• Data Analyst
• Systems Engineer
• Machine Learning / AI Engineer
• Data Scientist
• Data Engineer

Applied Data Science With Python syllabus

Module 1: Python Fundamentals for Data Science
• Introduction to Python
• Data types and variables
• Operators and expressions
• Control flow (if-else, loops)
• Functions
• Modules and packages
Module 2: Data Manipulation with Pandas
• Series and Data Frames
• Data cleaning and pre-processing
• Data aggregation and grouping
• Data merging and joining
• Time series analysis
Module 3: Data Visualization with Matplotlib and Seaborn
• Line plots, scatter plots, histograms, bar charts
• Subplots and grids
• Customization and styling
• Interactive visualizations (optional)
Module 4: Data Exploration and Analysis
• Exploratory data analysis (EDA)
• Descriptive statistics
• Hypothesis testing
• Correlation and regression analysis
Module 5: Machine Learning Algorithms
• Supervised learning (regression, classification)
• Unsupervised learning (clustering, dimensionality reduction)
• Model evaluation and selection
• Hyper parameter tuning
Module 6: Deep Learning with TensorFlow and Keras
• Neural networks
• Convolutional neural networks (CNNs)
• Recurrent neural networks (RNNs)
• Transfer learning
Module 7: Natural Language Processing (NLP)
• Text pre-processing
• Feature engineering
• Sentiment analysis
• Text classification
• Text generation
Module 8: Data Science Project Lifecycle and Tools
• Problem formulation
• Data collection and cleaning
• EDA and feature engineering
• Model building and evaluation
• Deployment and maintenance
• Data science tools (Jupyter Notebook, Git, cloud platforms)

FAQ's on Applied Data Science With Python training & certification

What is Applied Data Science with Python?
• Applied Data Science with Python refers to using Python programming to apply data science techniques to real-world problems. Python is a powerful tool that helps data scientists analyze large amounts of data, create predictive models, and visualize the results. It is widely used because of its flexibility and the availability of many libraries such as Pandas, NumPy, and Scikit-learn, which simplify data handling and analysis.
How useful is Python in Data Science?
• Python is extremely useful in data science due to its simplicity, readability, and strong community support. It has many built-in libraries that allow data scientists to handle data, perform complex calculations, and create visualizations. Python integrates well with other tools, making it ideal for the complete data science pipeline—from data collection to model deployment.
Should I Learn Python or Data Science First?
• You should learn Python first, as it will make understanding data science concepts easier. Python is widely used in data science, so having a basic understanding of programming will help you quickly apply data science methods. Once you’re comfortable with Python, learning data science techniques like data analysis, machine learning, and statistics will be smoother.
Is Data Science More Math or Coding?
• Data science is a combination of both math and coding. Math, especially statistics, is essential for understanding data and building models, while coding is necessary for implementing these models and working with data efficiently. Depending on the role, some data scientists may focus more on math (e.g., research roles), while others might do more coding (e.g., in applied or engineering roles).
Can You Do Data Science with Python?
• Yes, you can do data science with Python. Python is one of the most popular programming languages in data science because it’s versatile and easy to learn. From data cleaning and analysis to machine learning and data visualization, Python can handle every step of the data science process.
Is Python for Data Science Hard?
• Python is easy for beginners in data science, primarily since Python is known for being user-friendly. Many people who need programming experience find Python easy to pick up due to its simple syntax. While advanced data science concepts might take time to master, learning Python itself is relatively straightforward.
What is the Difference between Data Science and Applied Data Science?
• Data science is the broader field that uses algorithms, processes, and systems to extract insights from data. It includes theoretical aspects such as statistics and machine learning. On the other hand, applied data science focuses more on using these methods in practical, real-world scenarios to solve specific business or societal problems. Applied data science is more hands-on and implementation-oriented.
Is It Still Worth Becoming a Data Scientist?
• Yes, it is still worth becoming a data scientist. According to the U.S. Bureau of Labor Statistics, the demand for data scientists is expected to grow by 36% by 2031, much faster than the average for other occupations. In 2023, the average salary for data scientists in the USA was around $103,930 annually. This shows that the field remains highly lucrative and in demand, making it a promising career path.

About Applied Data Science with Python Certification

The Applied Data Science with Python certification is recognized globally, validating the holder's expertise in data science methodologies using Python. This credential demonstrates proficiency in essential skills such as statistical analysis, machine learning, and data visualization. Earning this certification enhances career prospects, as it is highly regarded by employers in the tech industry.

About Applied Data Science with Python Placement

The Applied Data Science with Python program offers comprehensive job placement assistance, ensuring that participants are well-prepared for the job market. Our counselors provide personalized support, helping students refine their resumes and interview preparation. This program connects learners with industry partners, facilitating networking opportunities and job placements in various data science roles.

About our Applied Data Science With Python course

Our Applied Data Science with Python course is designed to equip you with the essential skills needed to embark on a successful career in the field of data science. The course is tailored to suit both beginners and professionals alike, providing an in-depth understanding of Python programming and its application in data science.

Overview of our Applied Data Science With Python Training

This comprehensive training aims to provide a strong foundation in Python programming, data manipulation, data visualization, machine learning algorithms, and more. Our hands-on approach ensures you gain practical experience while mastering theoretical concepts, preparing you for real-world data science challenges.

Objectives of our Applied Data Science With Python training course

The primary objective of this course is to transform you into a complete data science professional. By the end of this course, you will be proficient in Python and its data science libraries, ready to analyze and interpret complex data using advanced machine learning models.

What you will learn in our Applied Data Science With Python classes

Python basics and its application in data science
Data manipulation and analysis with pandas
Data visualization with matplotlib and seaborn
Understanding of machine learning algorithms
Building predictive models using scikit-learn
Real-world project experience with Python

Course details

Course Duration: This is a 12-week intensive course.
Level: Beginner to Intermediate
Prerequisites: Basic understanding of programming concepts.
Certification: Upon successful completion of the course, you will be awarded a Certificate of Completion in Applied Data Science with Python.

Course Syllabus

Our Applied Data Science With Python Training covers the following modules:

Module 1: Python Basics
- Python installation and setup
- Introduction to Python programming
- Data types and variables
- Control structures
- Functions and modules
Module 2: Data Manipulation and Analysis
- Introduction to pandas
- Data cleaning and preparation
- Data wrangling and aggregation
- Time series analysis
- Advanced pandas
Module 3: Data Visualization
- Introduction to matplotlib and seaborn
- Creating various types of plots
- Advanced visualization techniques
- Interactive visualization
- Creating dashboards
Module 4: Machine Learning
- Understanding machine learning algorithms
- Supervised learning
- Unsupervised learning
- Building predictive models
- Evaluating model performance