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Data Science in Python

Data Science in Python

Data Science in Python

From Beginning

Data Science in Python Education

Master Data Science with Python in Ahmedabad – Join Patel Web Solution

Data Science is a powerful interdisciplinary field that combines statistics, machine learning, and data analysis techniques to extract meaningful insights from raw data. At Patel Web Solution, we offer a comprehensive Data Science in Python course in Ahmedabad, designed to equip you with the skills needed to thrive in today’s data-driven world.

Why Learn Data Science with Python?

Python has rapidly become the preferred language for data science thanks to its simplicity, versatility, and vast collection of specialized libraries.

Key Advantages of Using Python for Data Science:

Beginner-Friendly Syntax

Python’s clean and readable code structure makes it ideal for both beginners and experienced professionals.

Extensive Library Support

Work seamlessly with leading Python libraries:

  • NumPy – Efficient handling of large numerical datasets.
  • Pandas – Powerful tools for data manipulation and analysis.
  • Matplotlib & Seaborn – Create stunning and insightful visualizations.
  • Scikit-learn – Build and evaluate machine learning models with ease.
  • TensorFlow & PyTorch – Dive into deep learning and neural networks.

Strong Community & Resources

A large, active community ensures you have access to countless tutorials, forums, and documentation.

Easy Integration

Python integrates smoothly with tools and technologies like SQL, R, Excel, and big data platforms such as Apache Spark.

Versatility Across Domains

Whether you're cleaning data, performing statistical analysis, creating visualizations, or building AI models – Python does it all.

Real-World Applications You’ll Learn in Our Course:

  • Business forecasting and predictive analytics
  • Natural Language Processing (NLP)
  • Image and video recognition
  • Recommendation systems (like those used by Netflix and Amazon)
  • Financial modeling and risk analysis

Why Choose Our Python for Data Science Course in Ahmedabad?

At Patel Web Solution, we blend expert mentorship with hands-on projects and real-world case studies. Our curriculum is industry-oriented and regularly updated to match current trends. Whether you’re a student, working professional, or business owner looking to upskill, our course empowers you with practical knowledge that gets results.

Enroll Today and Start Your Data Science Journey!

Master Python and unleash the power of data with Ahmedabad’s trusted training provider – Patel Web Solution. Contact us now to book a free demo or learn more about our flexible course options.

Level : 1

Fundamental Languages

  • C,C++
  • Core Python
  • SQL

Level : 2

Data Analytics

  • Introduction to Data Science and Analytics
  • Overview of data science and its applications
  • Data analytics lifecycle
  • Introduction to Python for data analysis
  • Data Collection and Preprocessing
  • Data sources and collection methods
  • Data cleaning and preprocessing techniques
  • Handling missing data and outliers
  • Exploratory Data Analysis (EDA)
  • Descriptive statistics
  • Data visualization with Matplotlib and Seaborn
  • Correlation analysis
  • Statistical Analysis
  • Probability distributions
  • Hypothesis testing
  • Regression analysis (linear and multiple)
  • Data Visualization and Storytelling
  • Advanced visualization techniques
  • Creating interactive dashboards with Plotly and Dash
  • Effective data presentation and storytelling
  • Introduction to Machine Learning
  • Supervised vs. unsupervised learning
  • Model evaluation and validation
  • Basic classification and regression algorithms

Level : 3

Advanced Machine Learning

Advanced Machine Learning Algorithms

  • Ensemble methods (Random Forests, Gradient Boosting)
  • Support Vector Machines
  • Neural Networks and Deep Learning basics
  • Feature Engineering and Selection
  • Feature creation and transformation
  • Dimensionality reduction techniques (PCA, t-SNE)
  • Feature importance and selection methods
  • Model Optimization and Hyperparameter Tuning
  • Cross-validation techniques
  • Grid search and random search
  • Bayesian optimization

Advanced Topics

  • Natural Language Processing
  • Text preprocessing and tokenization
  • Forecasting methods (ARIMA, Prophet)
  • Sentiment analysis
  • Topic modeling
  • Time Series Analysis
  • Time series decomposition
  • Anomaly detection
  • Ethics and Responsible AI
  • Bias and fairness in machine

Technologies Used:

  • Python
  • Jupyter Notebooks
  • NumPy
  • Pandas
  • Matplotlib
  • Seaborn
  • Scikit-learn TensorFlow or PyTorch
  • Plotly and Dash
  • Apache Spark
  • SQL (e.g., PostgreSQL)
  • NoSQL (e.g., MongoDB)
  • Git for version control

Hands-on Projects:

  • Customer Churn Prediction: Analyze customer data to predict which customers are likely to churn and develop retention strategies.
  • Stock Price Prediction: Build a time series forecasting model to predict stock prices using historical data and external factors.
  • Sentiment Analysis of Social Media Data: Analyze social media posts to determine public sentiment about a product or brand.
  • Recommendation System: Develop a collaborative filtering-based recommendation system for an e-commerce platform.

Duration : 6 to 8 Months

...

Can I Get a Free Demo Lecture before joining your Institute?

Yes, Sure. You can attend a Free Demo Lecture.


Can You Provide a Certificate after Training Completion?

Yes, We will Provide ISO 9001:2015, Government Approved Certificate.


Can I Pay Fees through EMI?

Yes, you Can Pay your Fees in EMI options.


Can I get a good Discount in Course Fees?

Yes, you will get a good Discount in One Short Payment Option.


Can any Non IT Students can join your Institute?

Yes,our 50% students are from Non IT Background.


Can I get a Job Placement?

Yes, 100%. We have our own Job Placement Consultancy – My Job Placement.


Is there any Soft skill Training for Job Placement?

Yes, we are providing FREE Spoken English Sessions, Interview Preparation & Mock Round for Interviews.


Can you adjust my Timing for Training Session?

Yes Sure, We arrange Our Batches according College Students & Working Professionals.


Is my Course will run in fix Time duration?

As per our standard Rules, We have decided a fix duration for every courses. But if any student requires a few more time then no problem.


Can you provide an Internship?

Yes, We are providing 15/45 Days Internship & 3 to 12 Months Internship also we are providing with Live Project Training & Job Placement.

What is Data Science?

Data Science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It combines techniques from statistics, mathematics, programming, and domain expertise to analyze data and inform decision-making.

What are the main libraries used in Data Science with Python?

Some of the main libraries used in Data Science with Python include:

  • NumPy: For numerical computations and handling large arrays.
  • Pandas: For data manipulation and analysis using DataFrames.
  • Matplotlib: For creating static visualizations.
  • Seaborn: For statistical data visualization built on top of Matplotlib.
  • Scikit-learn: For machine learning and data mining.
  • TensorFlow and PyTorch: For deep learning applications.

What is Pandas, and how is it used in Data Science?

Pandas is a Python library used for data manipulation and analysis. It provides data structures like Series (1D) and DataFrame (2D) that make it easy to handle and analyze large datasets, perform data cleaning, and conduct exploratory data analysis.

What is the difference between a DataFrame and a Series in Pandas?

  • Series: A one-dimensional labeled array capable of holding any data type (integers, strings, etc.). It is similar to a single column in a spreadsheet.
  • A two-dimensional labeled data structure with columns of potentially different types, similar to a table in a database or a spreadsheet.

What is the purpose of the train_test_split() function in Scikit-learn?

The train_test_split() function is used to split a dataset into two parts: a training set and a testing set. This is important for evaluating the performance of machine learning models, allowing you to train the model on one subset of data and test it on another to assess its generalization ability.

What is overfitting in machine learning?

Overfitting occurs when a machine learning model learns the training data too well, capturing noise and outliers instead of the underlying pattern. This results in poor performance on unseen data. Techniques like crossvalidation, pruning, and regularization can help mitigate overfitting.

What are some common methods for handling missing data in a dataset?

Common methods for handling missing data include:

  • Removing missing values: Discarding rows or columns with missing data.
  • Imputation: Filling missing values with statistical measures (mean, median, mode) or using algorithms to predict missing values.
  • Flagging: Adding a new column to indicate if a value was missing.

What is feature scaling, and why is it important?

Feature scaling refers to the process of normalizing or standardizing the range of independent variables (features) in a dataset. It is important because many machine learning algorithms (like gradient descent) perform better when features are on a similar scale, improving convergence speed and model performance.

What is the difference between supervised and unsupervised learning?

  • Supervised Learning: Involves training a model on labeled data, where the output is known. Common tasks include classification and regression.
  • Unsupervised Learning: Involves training a model on unlabeled data, where the output is not known. The model tries to find patterns or groupings in the data. Common tasks include clustering and dimensionality reduction.

What is the purpose of the groupby() function in Pandas?

The groupby() function in Pandas is used to split the data into groups based on certain criteria. It allows for aggregate operations to be performed on each group, enabling analysis of summary statistics (like mean, sum, count) for different subsets of the data.

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