Data Science Training

Categories Other Courses
Course level:Intermediate
Data Science Training programs are designed to equip individuals with the skills and knowledge necessary to analyze and interpret complex data. These programs typically cover a range of topics from basic statistical concepts to advanced machine learning algorithms and tools.
Data Science Training
Data Science Training Learn Online

Why should you choose Nisa For Data Science Training?

Nisa Trainings is the best online training platform for conducting one-on-one interactive live sessions with a 1:1 student-teacher ratio. You can gain hands-on experience by working on near-real-time projects under the guidance of our experienced faculty. We support you even after the completion of the course and happy to clarify your doubts anytime. Our teaching style at Nisa Trainings is entirely hands-on. You’ll have access to our desktop screen and will be actively conducting hands-on labs on your desktop.

Job Assistance

If you face any problem while working on Data Science Course, then Nisa Trainings is simply a Call/Text/Email away to assist you. We offer Online Job Support for professionals to assist them and to solve their problems in real-time.

The Process we follow for our Online Job Support Service:

  • We receive your inquiry for Online Job
  • We will arrange a telephone call with our consultant to grasp your complete requirement and the tools you’re
  • If our consultant is 100% confident in taking up your requirement and when you are also comfortable with our consultant, we will only agree to provide service. And then you have to make the payment to get the service from
  • We will fix the timing for Online Job Support as mutually agreed by you and our consultant.

Course Information

Data Science Training
Duration: 25 Hours
Timings: Weekdays (1-2 Hours per day) [OR] Weekends (2-3 Hours per day)
Training Method: Instructor Led Online One-on-One Live Interactive
Sessions.

COURSE CONTENT :

 
Module 1: Introduction to Data Science
  • Overview of Data Science: Definition, importance, applications in different industries.
  • Data Science Process: Understanding the complete workflow—data collection, cleaning, exploration, modeling, and interpretation.
  • Data Science Tools: Introduction to Python, R, SQL, and Jupyter Notebooks.
  • Overview of Data Types: Structured vs. unstructured data, time series, text data, image data.
Module 2: Python/R Programming for Data Science
  • Python Basics: Data types, variables, operators, conditionals, loops, and functions.
  • R Programming: Data structures (vectors, data frames, lists), basic operations, control flow.
  • Data Structures: Lists, tuples, dictionaries (Python) or vectors, data frames, matrices (R).
  • Libraries/Packages: pandas, numpy, matplotlib, seaborn (Python); dplyr, ggplot2 (R).
Module 3: Data Wrangling & Preprocessing
  • Data Collection: Importing data from various formats (CSV, Excel, SQL, JSON).
  • Data Cleaning: Handling missing values, duplicates, outliers, and inconsistent data.
  • Data Transformation: Normalization, scaling, encoding categorical variables.
  • Data Merging and Aggregation: Merging datasets, group by operations, pivot tables.
  • Handling Time Series Data: Date-time manipulations and analysis.
Module 4: Exploratory Data Analysis (EDA)
  • Descriptive Statistics: Mean, median, mode, variance, standard deviation.
  • Data Visualization: Creating histograms, box plots, bar charts, line plots, and heatmaps.
  • Identifying Patterns: Correlation, trends, and outliers in data.
  • Dimensionality Reduction: PCA (Principal Component Analysis) for feature reduction.
Module 5: Statistical Analysis
  • Probability Fundamentals: Probability distributions (normal, binomial, Poisson).
  • Hypothesis Testing: Null and alternative hypothesis, t-tests, chi-square tests, ANOVA.
  • Statistical Significance: P-values, confidence intervals, error types (Type I & Type II).
  • Regression Analysis: Simple linear regression, multiple linear regression.
Module 6: Machine Learning – Supervised Learning
  • Introduction to Machine Learning: Overview of supervised vs. unsupervised learning.
  • Regression Models: Linear regression, logistic regression, evaluation metrics (MSE, RMSE, R²).
  • Classification Models: Decision trees, k-Nearest Neighbors (k-NN), Support Vector Machines (SVM).
  • Model Evaluation: Accuracy, precision, recall, F1 score, confusion matrix, ROC curve, AUC.
Module 7: Machine Learning – Unsupervised Learning
  • Clustering Techniques: K-Means clustering, hierarchical clustering, DBSCAN.
  • Dimensionality Reduction: PCA (Principal Component Analysis), t-SNE.
  • Anomaly Detection: Identifying outliers and unusual data points.
Module 8: Advanced Machine Learning Techniques
  • Ensemble Learning: Random Forest, Gradient Boosting (XGBoost, LightGBM).
  • Support Vector Machines: Theory, kernels, and implementation.
  • Model Tuning: Hyperparameter optimization using Grid Search, Random Search.
  • Cross-validation: k-fold cross-validation for model selection and evaluation.
Module 9: Deep Learning and Neural Networks
  • Introduction to Neural Networks: Basic architecture and components (neurons, activation functions, layers).
  • Training Neural Networks: Forward and backward propagation, gradient descent.
  • Deep Learning Frameworks: TensorFlow, Keras, and PyTorch.
  • Types of Neural Networks: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTMs.
Module 10: Big Data Technologies and Cloud Computing
  • Big Data Concepts: Characteristics of big data, Hadoop ecosystem, HDFS, MapReduce.
  • Apache Spark: Basics of Spark, RDDs, Spark SQL, Spark MLlib for machine learning.
  • Cloud Platforms: Introduction to AWS, Google Cloud, Microsoft Azure for data science applications.
  • Distributed Computing: Parallel processing and distributed algorithms.
Module 11: Data Visualization & Communication
  • Visualization Principles: Effective charts and graphs, avoiding misleading visualizations.
  • Interactive Dashboards: Using Tableau, Power BI for building interactive dashboards.
  • Advanced Visualizations: Creating dynamic plots with Plotly, Bokeh, and Dash.
  • Presenting Insights: Communicating data-driven insights to non-technical stakeholders.
Module 12: Data Science in Practice
  • Data Science Workflow: Real-world applications of the data science pipeline.
  • Case Studies: Industry-specific use cases (finance, healthcare, e-commerce, etc.).
  • Collaboration: Working in teams, version control using Git, documenting projects.
  • Ethics and Bias: Ethical considerations in data collection, model fairness, and bias mitigation.
Module 13: Capstone Project
  • Project Work: A hands-on project involving end-to-end data science work—data collection, cleaning, modeling, and deployment.
  • Problem Solving: Analyzing and solving real-world data challenges.
  • Deployment: Introduction to deploying machine learning models into production (using tools like Flask, Docker).
  • Presentation: Prepare and present the project outcomes, insights, and model performance.
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