- Descriptive Analytics: This type summarizes past data and helps to understand what has happened by providing insights through visualizations and dashboards.
Diagnostic Analytics: It goes a step further by identifying the reasons behind certain outcomes, helping organizations understand why something happened.
Predictive Analytics: By using statistical models and machine learning techniques, predictive analytics forecasts future trends, helping businesses anticipate potential opportunities or risks.
Prescriptive Analytics: This type suggests the best course of action based on the data insights, often through optimization models or simulation algorithms, to guide decision-making in real-time.
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Data Analytics Using Python Course
DURATION
6 Months
MODE OF TRAINING
Online/Offline
LEVEL
Advanced
Data Analytics Using Python Course Overview
Learn to extract insights from raw data using Python programming. Master data cleaning, preprocessing, and visualization techniques with Pandas, NumPy, and Matplotlib. Explore statistical analysis and predictive modeling to make informed decisions. Work on real-world projects in fields like finance, marketing, and healthcare. Understand the basics of machine learning for data-driven problem-solving. Develop proficiency in presenting data through dashboards and reports. This course is perfect for aspiring data analysts looking to drive business intelligence.
Key Libraries in Python for Data Analytics:
SciPy
Builds on NumPy and provides additional functionality for scientific and technical computing, including optimization, integration, interpolation, eigenvalue problems, and more.
Scikit-learn
A machine learning library used for data mining and data analysis. It offers simple and efficient tools for data mining and data analysis, and it includes algorithms for classification, regression, clustering, and more.
Statsmodels
Provides classes and functions for statistical modeling. It supports linear and logistic regression, time-series analysis, hypothesis testing, etc.
Jupyter Notebook
An interactive web-based environment for running Python code, visualizing data, and sharing results. Ideal for data analysis projects as it allows you to combine code, text, and visualizations.
Data Analytics
Types of Data Analytics
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Data Preprocessing
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- - Data cleaning - Handling missing data - Outlier detection and removal - Data transformation (scaling, normalization) - Feature engineering - Data encoding (one-hot, label encoding)
Exploratory Data Analysis (EDA)
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- - Descriptive statistics (mean, median, mode, variance) - Data visualization (bar charts, histograms, scatter plots) - Correlation analysis (Pearson, Spearman) - Distribution analysis (normal distribution, skewness) - Box plots and Violin plots - Heatmaps and Pair plots.
Statistical Analysis
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- - Hypothesis testing (T-test, Chi-square test) - ANOVA (Analysis of Variance) - Confidence intervals and p-values - Bayesian statistics - Statistical distributions (Normal, Binomial, Poisson).
Machine Learning Models
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- - Supervised learning - Regression (Linear, Polynomial, Logistic) - Classification (Decision Trees, SVM, KNN, Naive Bayes) - Unsupervised learning - Clustering (K-means, Hierarchical, DBSCAN) - Dimensionality reduction (PCA, t-SNE) - Model evaluation (accuracy, precision, recall, F1 score) - Overfitting vs underfitting - Cross-validation (K-fold, LOOCV).
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