| Course Level: | Beginner to Advanced |
| Course Duration: | 1 to 3 Months | Approximately |
| Training Days: | Flexible Schedule (Monday-Friday) |
| Training Time: | 1 Session / Day (45min-1hr) |
| Course Mode: | Dedicated Virtual class (Online) |
| Course Type: | Skill Oriented Customised Training |
| Course Start On: | On Registration | Within 5 working days |
| Class Size: | 1 to 1 | No Groups | No Batch |
Understanding the role and importance of data science
Overview of the data science process
Introduction to Python programming for data science
Introduction to data manipulation and cleaning
The Data Science Training Course is designed to equip participants with the necessary skills and knowledge to become proficient data scientists. The course covers a comprehensive range of topics and techniques used in the field of data science, including data analysis, machine learning, statistical modeling, and data visualization. Participants will gain hands-on experience by working on real-world data projects and using industry-standard tools and technologies. The course is suitable for beginners with a basic understanding of programming and statistics.
Techniques for exploratory data analysis
Visualization tools and libraries
Data preprocessing and feature engineering
Understanding statistical concepts and their applications in data science
Probability theory and distributions
Hypothesis testing and statistical inference
Introduction to supervised and unsupervised learning
Linear regression and logistic regression
Decision trees and random forests
Evaluation metrics for machine learning models
Support Vector Machines (SVM)
Ensemble methods (e.g., bagging, boosting)
Using advanced DAX functions
Advanced visualizations (maps, gauges, cards)
Drill-through and drill-down techniques
Clustering algorithms (e.g., K-means, hierarchical clustering)
Dimensionality reduction techniques (e.g., PCA, t-SNE)
Introduction to neural networks and deep learning
Building and training neural networks using TensorFlow or PyTorch
Convolutional Neural Networks (CNNs) for image classification
Recurrent Neural Networks (RNNs) for sequence data analysis
Introduction to big data and its challenges
Working with distributed computing frameworks (e.g., Apache Spark)
Handling large-scale datasets and parallel processing
Introduction to NLP and its applications
Text preprocessing and feature extraction
Building NLP models (e.g., sentiment analysis, text classification)
Understanding time series data
Techniques for time series forecasting
ARIMA modeling and seasonal decomposition
Ethical considerations in data science
Privacy and data protection regulations
Bias and fairness in machine learning algorithms
The course content can be customized or expanded based on the specific requirements and goals of the training program.
No limits on learning, no limits on duration, no limits on salary, no limits on interviews, learn as much as you can & get ready for your first job.
Flexible training duration
Weekday | Weekend | On avalability
Practical based approach
Individual 1 to 1 dedicated training
Professional developers as your trainer
Skill oriented customised training on your need
Free post training support
6 months training duration
Monday to Friday (Regular office)
Live & Direct work with team
Individual 1 to 1 training
+Unlimited placement, Dual job opportunity.
Get your first job offer on the day of joining.
IN as fresher OUT as experienced developer
We strive to provide quality of learning step by step, that exactly what you want!
