Last Updated on December 10, 2023 by GeeksGod

Course : Python for Data Science – NumPy, Pandas & Scikit-Learn

Python for Data Science – NumPy, Pandas & Scikit-Learn

The “Python for Data Science – NumPy, Pandas & Scikit-Learn” course is a comprehensive guide to Python’s most powerful data science libraries, designed to provide you with the skills necessary to tackle complex data analysis projects. Whether you are a beginner or an experienced programmer, this course will help you diversify your skill set and learn how to manipulate, analyze, and visualize data using Python.

Introduction to Data Science

Data science is a rapidly growing field that combines mathematics, statistics, programming, and domain knowledge to extract valuable insights from complex datasets. As a data scientist, you will utilize various analytical techniques, statistical models, and machine learning algorithms to discover patterns, trends, and correlations within the data.

The Role of a Data Scientist

A data scientist plays a crucial role in organizations across various industries such as finance, healthcare, marketing, and technology. They are responsible for tasks such as data collection, cleaning, exploratory data analysis, feature engineering, and building predictive or prescriptive models. Data scientists work closely with stakeholders to understand business needs, formulate data-driven strategies, and communicate findings effectively to support decision-making processes.

Skills required for Data Science

In addition to strong analytical and problem-solving skills, data scientists need a deep understanding of statistical concepts and programming languages such as Python or R. They should also be proficient in data manipulation, data visualization, and machine learning techniques.

Numpy for Data Analysis

Numpy is the fundamental package for numerical computing in Python. It provides powerful tools for working with arrays and array-oriented computing, which are crucial for performance-intensive data analysis. Here are some topics covered in the NumPy exercises:

Topics covered in NumPy exercises:

• Working with numpy arrays
• Generating numpy arrays
• Generating numpy arrays with random values
• Iterating through arrays
• Dealing with missing values
• Working with matrices
• Reading/writing files
• Joining arrays
• Reshaping arrays
• Computing basic array statistics
• Sorting arrays
• Filtering arrays
• Image as an array
• Linear algebra
• Matrix multiplication
• Determinant of the matrix
• Eigenvalues and eigenvectors
• Inverse matrix
• Shuffling arrays
• Working with polynomials
• Working with dates
• Working with strings in arrays
• Solving systems of equations

Pandas for Data Manipulation

Pandas is a powerful library designed for data manipulation and analysis. It provides easy-to-use data structures, such as Series and DataFrames, and offers a wide range of operations for handling missing data, merging datasets, filtering data, and performing statistical analysis. Here are some topics covered in the Pandas exercises:

Topics covered in Pandas exercises:

• Working with Series
• Working with DatetimeIndex
• Working with DataFrames
• Reading/writing files
• Working with different data types in DataFrames
• Working with indexes
• Working with missing values
• Filtering data
• Sorting data
• Grouping data
• Mapping columns
• Computing correlation
• Concatenating DataFrames
• Calculating cumulative statistics
• Working with duplicate values
• Preparing data for machine learning models
• Dummy encoding
• Working with CSV and JSON files
• Merging DataFrames
• Pivot tables

Scikit-Learn for Machine Learning

Scikit-Learn is a powerful library providing efficient tools for machine learning and statistical modeling. It offers a broad range of algorithms for classification, regression, clustering, and dimensionality reduction. In addition, Scikit-Learn provides functions for data preprocessing, model selection, and evaluation. Here are some topics covered in the Scikit-Learn exercises:

Topics covered in Scikit-Learn exercises:

• Preparing data for machine learning models
• Working with missing values
• Classification, regression, and clustering
• Discretization
• Feature extraction
• Polynomial Features
• LabelEncoder
• OneHotEncoder
• StandardScaler
• Dummy encoding
• Splitting data into train and test sets
• Logistic Regression
• Confusion matrix
• Classification report
• Linear Regression
• MAE – Mean Absolute Error
• MSE – Mean Squared Error
• Sigmoid() function
• Entropy
• Accuracy score
• Decision Tree Classifier
• GridSearchCV
• Random Forest Classifier
• CountVectorizer
• TfidfVectorizer
• KMeans
• Agglomerative Clustering
• Hierarchical Clustering
• DBSCAN
• Dimensionality reduction, PCA analysis
• Association Rules
• Local Outlier Factor
• Isolation Forest
• KNeighborsClassifier
• MultinomialNB
• Gradient Boosting Regressor

By the end of the “Python for Data Science – NumPy, Pandas & Scikit-Learn” course, you will have a firm grasp of how to use Python’s primary data science libraries to conduct sophisticated data analysis. This will equip you with the knowledge and skills to undertake your own data-driven projects.

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