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Python Programming For Data Science

Master the fundamentals of Python programming specifically tailored for data science applications. This course is designed to equip you with the essential Python skills required to analyze data, build models, and extract meaningful insights. Learn how to manipulate data using libraries like NumPy, Matplotlib, and Seaborn, and gain hands-on experience with real-world datasets. Whether you're a beginner or transitioning into data science, this course provides a strong foundation in Python for data-driven decision-

Course

4.8 (4178)

Learners

5720

MNC's Expert Trainer

Exp. 15+Yrs.

Upskill with

Internship

What’s included in this Course

1 months duration hands-on practice

Live project training

Interview Preparations

150+ Assignments

Online & Offline Training

500+ Questions for Exercise

Schedule Your Free Trial Class

  8130903525      8130805525

Core Python Programming Certification

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Core Python Programming is the foundation of modern software development, offering simplicity, readability, and power for both beginners and experienced programmers. This language is widely used in various domains such as web development, data science, machine learning, automation, and artificial intelligence. Our Core Python course is designed to help students build a strong programming base by understanding key concepts like variables, data types, operators, loops, and conditional statements. Python’s easy syntax and extensive community support make it the ideal first programming language.

The course begins with the fundamentals of Python programming, gradually moving into more complex topics like functions, modules, exception handling, and file operations. Learners will also explore built-in data structures such as lists, tuples, dictionaries, and sets, all of which are essential for real-world application development. Practical exercises and hands-on coding sessions are an integral part of our training, ensuring that students not only understand the theory but also gain confidence in writing efficient Python code.

  • Overview of Python: Understand Python’s history, features, and its significance in data science applications.
  • Installing Python: Set up Python using Anaconda distribution and configure Jupyter Notebook for interactive coding.
  • First Program: Write and execute a "Hello, World!" program to understand Python’s basic syntax and execution.
  • Python Interpreters and IDEs: Explore tools like PyCharm and VS Code for efficient coding and debugging.

  • Variables: Declare and use variables for integers, floats, strings, and booleans.
  • Basic Operations: Perform arithmetic (e.g., +, -, *, /), comparison (e.g., ==, >), and logical operations (e.g., and, or).
  • Comments: Use single-line and multi-line comments to enhance code readability.
  • Type Conversion: Convert between data types using functions like int(), float(), and str().

  • Conditional Statements: Use if, elif, and else for decision-making.
  • Loops: Implement for loops for iteration and while loops for condition-based repetition.
  • Loop Control: Apply break, continue, and pass to control loop execution.
  • Exercises: Build a simple calculator and a number guessing game to apply control structures.

  • Lists: Create, index, slice, and modify lists using methods like append() and pop().
  • Tuples: Understand immutability and use cases for tuples in data storage.
  • Dictionaries: Manage key-value pairs, accessing and updating data with methods like get().
  • Sets: Use sets for unique elements and perform operations like union and intersection.
  • Exercises: Create a shopping list manager and organize basic data structures.

  • Defining Functions: Create functions using def and call them with arguments.
  • Parameters: Use positional, keyword, and default arguments in functions.
  • Scope: Understand local and global variable scopes and their lifetimes.
  • Lambda Functions: Write concise anonymous functions using lambda.
  • Exercises: Build reusable functions for calculations, such as computing factorials.

  • User Input: Capture input using input() and process it.
  • File Operations: Read from and write to text files using open().
  • CSV Files: Handle CSV data for simple data storage and retrieval.
  • Exercises: Create a data logger to store user inputs in a file.

  • List Comprehensions: Write concise list creations using comprehensions.
  • Nested Structures: Work with nested lists and dictionaries for complex data.
  • Collections Module: Use Counter, defaultdict, and namedtuple for advanced data handling.
  • Exercises: Aggregate and transform data using advanced structures.

  • Exceptions: Handle errors using try-except blocks.
  • Custom Exceptions: Raise and define custom exceptions for specific cases.
  • Debugging: Use print statements and debuggers to trace and fix code issues.
  • Exercises: Build robust programs with comprehensive error handling.

  • Arrays: Create and manipulate NumPy arrays with indexing and slicing.
  • Array Operations: Perform broadcasting and element-wise operations.
  • Mathematical Functions: Use NumPy for linear algebra and statistical operations.
  • Exercises: Preprocess data using NumPy array manipulations.

  • DataFrames and Series: Create and manipulate Pandas data structures.
  • Data Cleaning: Handle missing values and remove duplicates.
  • Data Operations: Filter, group, and merge datasets.
  • Exercises: Analyze a sample dataset, such as sales data.

  • Basic Plots: Create line, scatter, and bar plots using Matplotlib.
  • Customization: Add labels, titles, and legends to plots.
  • Advanced Visualizations: Use Seaborn for heatmaps, box plots, and pair plots.
  • Exercises: Visualize trends in datasets for insights.

  • Custom Modules: Create and import custom Python modules.
  • Standard Library: Explore modules like math, random, and datetime.
  • Package Management: Install and manage packages with pip and conda.
  • Exercises: Build a modular data processing pipeline.

  • Advanced Indexing: Use MultiIndex and pivot tables for complex data.
  • Time Series: Analyze time series data with resampling and rolling windows.
  • Large Datasets: Optimize memory usage with chunking techniques.
  • Exercises: Clean and analyze real-world datasets (e.g., Kaggle).

  • Supervised Learning: Implement linear and logistic regression models.
  • Unsupervised Learning: Apply K-means clustering and PCA.
  • Model Evaluation: Use train-test splits, cross-validation, and metrics like accuracy.
  • Exercises: Build a predictive model for a dataset.

  • Feature Scaling: Normalize and standardize data for machine learning.
  • Categorical Encoding: Convert categorical variables using one-hot encoding.
  • Feature Selection: Reduce dimensionality with techniques like PCA.
  • Exercises: Prepare data for machine learning models.

  • SQL Databases: Connect using sqlite3 and SQLAlchemy.
  • SQL Queries: Write and execute queries to extract data.
  • Data Integration: Load database data into Pandas DataFrames.
  • Exercises: Manage a database for a small project.

  • Neural Networks: Build models with layers and activation functions.
  • Training: Train and evaluate deep learning models.
  • Overfitting: Apply regularization and dropout to improve models.
  • Exercises: Perform image classification or time series prediction.

  • Web Scraping: Extract data using BeautifulSoup and Scrapy.
  • APIs: Fetch data from APIs using requests.
  • Data Formats: Handle JSON and XML data formats.
  • Exercises: Scrape a website or fetch data from a public API.

  • Ensemble Methods: Implement random forests and gradient boosting (XGBoost, LightGBM).
  • Hyperparameter Tuning: Use grid search and random search for optimization.
  • Imbalanced Data: Handle imbalanced datasets with SMOTE and oversampling.
  • Exercises: Build a high-accuracy classifier.

  • PySpark: Use PySpark for distributed computing on large datasets.
  • Dask: Process large datasets with Dask for parallel computing.
  • Parallel Computing: Understand basics of parallel processing.
  • Exercises: Analyze a large dataset using PySpark.

  • REST APIs: Create APIs with Flask or FastAPI for model serving.
  • Cloud Deployment: Deploy models on platforms like AWS or Heroku.
  • Docker: Use Docker for containerization of applications.
  • Exercises: Deploy a machine learning model as an API.

  • Task Automation: Automate tasks using schedule and apscheduler.
  • Data Pipelines: Build pipelines with Airflow for workflow management.
  • Version Control: Use Git and GitHub for code management.
  • Exercises: Automate a data preprocessing pipeline.

  • Interactive Visualizations: Create plots with Plotly and Bokeh.
  • Dashboards: Build interactive dashboards using Dash or Streamlit.
  • Exercises: Create an interactive dashboard for a dataset.

  • End-to-End Project: Execute a complete data science project from data collection to deployment.
  • Example Projects: Build systems for predictive maintenance, customer segmentation, or recommendation systems.
  • Presentation: Document and present project results effectively.
  • Exercises: Complete a real-world data science project.

At HighTech Solutions Best IT Company & Training Institute, our Placement Assistance Program ensures that our students get placed in top IT companies with attractive salary packages.

Our Alumni Work In-

Entry-Level

0-2 years

💰 ₹3-6 LPA

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2-5 years

💰 ₹6-12 LPA

Senior-Level1

5-10 years

💰 ₹12-18 LPA

Senior-Level2

10-20 years

💰 ₹18-24 LPA

Management-Level

20+ years

💰 ₹25+ LPA

International

Global Opportunities

💰 $80K - $150K per year

Internship Programs

Paid/Unpaid

💰 8k-15k/Month

Freelancing

Effort Basis

💰 Hourly Payments

HighTech Solutions, based in Delhi NCR, offers a variety of IT courses designed to enhance the skills of both beginners and seasoned professionals. While specific salary packages for IT professionals associated with HighTech Solutions are not publicly disclosed, copmleting their industry-recognized training programs can significantly boost your earning potential in the IT sector.

Career Growth in Professional IT Courses

Become A Full Stack Developer
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Digital Marketing Specialist

Courses Offered By S&H HighTech Solutions

🌟 Full Stack Web Development 🌟 C and C++ Programming 🌟 Angular Training 🌟 Machine Learning with Python 🌟 React.js Training 🌟 Short-term Course (BCC, DCA, DFA, ADCA) 🌟 Advanced Excel & Data Visualization 🌟 Android App Development (Java, Kotlin) 🌟 iOS App Development 🌟 Cyber Security & Ethical Hacking 🌟 Beginner-Level Programming & IT Courses
🌟 Digital Marketing & SEO 🌟 Front-end Development 🌟 Database Training(SQL) 🌟 DevOps & CI/CD Pipelines 🌟 Microsoft Power BI & Tableau 🌟 Tableau For Data Analytics 🌟 E-Accounting & Tally ERP 🌟 Java Full Stack Development 🌟 .NET Development (C#, ASP.NET, .NET Core) 🌟 Artificial Intelligence & Deep Learning 🌟 Blockchain Development 🌟 Big Data & Hadoop
🌟 Graphic Design 2D & 3D Animation 🌟 Cloud Computing 🌟 Data Science & Data Analytics 🌟 Linux Administration & Shell Scripting 🌟 Automation / Robotics Process Automation 🌟 R Programming For Data Analytics 🌟 UX and UI Design 🌟 MERN Stack Development 🌟 MEAN Stack Development
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