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

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The Python for Data Science – Num Py, 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. This course is tailored for beginners who want to delve into the world of data science, as well as experienced programmers who wish to diversify their skill set. You will learn to manipulate, analyze, and visualize data using Python, a leading programming language for data science. The course begins with an exploration of Num Py, the fundamental package for numerical computing in Python. You’ll gain a strong understanding of arrays and array-oriented computing which is crucial for performance-intensive data analysis. The focus then shifts to Pandas, a library designed for data manipulation and analysis. You’ll learn to work with Series and Data Frames, handle missing data, and perform operations like merge, concatenate, and group by. The final section of the course is dedicated to Scikit-Learn, a library providing efficient tools for machine learning and statistical modeling. Here you’ll delve into data preprocessing, model selection, and evaluation, as well as a broad range of algorithms for classification, regression, clustering, and dimensionality reduction. By the end of the Python for Data Science – Num Py, 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, equipping you with the knowledge to undertake your own data-driven projects. Data Scientist – Unveiling Insights from Data Universe! A data scientist is a skilled professional who leverages their expertise in mathematics, statistics, programming, and domain knowledge to extract meaningful insights and valuable knowledge from complex datasets. They 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 involves tasks such as data collection, data cleaning, exploratory data analysis, feature engineering, and building predictive or prescriptive models. They work closely with stakeholders to understand business needs, formulate data-driven strategies, and communicate findings effectively to support decision-making processes. Data scientists possess strong analytical and problem-solving skills, as well as a deep understanding of statistical concepts and programming languages such as Python or R. They are proficient in data manipulation, data visualization, and machine learning techniques. In addition to technical skills, data scientists possess strong communication and storytelling abilities. They can translate complex data findings into actionable insights and effectively communicate them to both technical and non-technical audiences. Data scientists play a crucial role in various industries, including finance, healthcare, marketing, technology, and more. They help organizations make informed decisions, optimize processes, identify new opportunities, and solve complex problems by harnessing the power of data. Some topics you will find in the Num Py exercises: working with numpy arraysgenerating numpy arraysgenerating numpy arrays with random valuesiterating through arraysdealing with missing valuesworking with matricesreading/writing filesjoining arraysreshaping arrayscomputing basic array statisticssorting arraysfiltering arraysimage as an arraylinear algebramatrix multiplicationdeterminant of the matrixeigenvalues and eignevectorsinverse matrixshuffling arraysworking with polynomialsworking with datesworking with strings in arraysolving systems of equations Some topics you will find in the Pandas exercises: working with Seriesworking with Datetime Indexworking with Data Framesreading/writing filesworking with different data types in Data Framesworking with indexesworking with missing valuesfiltering datasorting datagrouping datamapping columnscomputing correlationconcatenating Data Framescalculating cumulative statisticsworking with duplicate valuespreparing data to machine learning modelsdummy encodingworking with csv and json fillesmerging Data Framespivot tables Topics you will find in the Scikit-Learn exercises: preparing data to machine learning modelsworking with missing values, Simple Imputer classclassification, regression, clusteringdiscretizationfeature extraction Polynomial Features class Label Encoder class One Hot Encoder class Standard Scaler classdummy encodingsplitting data into train and test set Logistic Regression classconfusion matrixclassification report Linear Regression class MAE – Mean Absolute Error MSE – Mean Squared Errorsigmoid() functionentorpyaccuracy score Decision Tree Classifier class Grid Search CV class Random Forest Classifier class Count Vectorizer class Tfidf Vectorizer class KMeans class Agglomerative Clustering class Hierarchical Clustering class DBSCAN classdimensionality reduction, PCA analysis Association Rules Local Outlier Factor class Isolation Forest class KNeighbors Classifier class Multinomial NB class Gradient Boosting Regressor class