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— pantech elearning —
Advance Certification Program in Industry
Data Science 2.0
Career-Based Complete Data Science Training: Algorithms, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning
45 Hours Total Length
Starts On May, 26th
Lifetime Access
everything you need to
Become a Successful Data scientist?
13+ Core Conepts
30+ Projects
Live / Recorded
Lifetime Access
Community Suported
PPTS & Downloadables
Way to Data scientist...
Step by Step
Step by Step Learning Process
Pre-Process & Understanding Data
Data Preparation
✓ Perform OOPS Concepts in Python
✓ Carry out cluster and factor analysis
✓ Be able to understand Machine Learning algorithms NumPy, stats models, and sci-kit-learn
✓ Apply your skills to real-life cases & examples
Data Processing
✓ The course provides the entire Knowledge data science
✓ Fill up your resume with in-demand data science tools & skills
Python programming with NumPy, pandas, matplotlib, and Seaborn,
Advanced statistical analysis tools: Tableau, stats models and scikit-learn, and TensorFlow
✓ Understand the mathematics behind Machine Learning
Data Execution
✓ Deep Learning frameworks: Google’s TensorFlow & Keras
✓ Develop coding and solving tasks with Algorithms
✓ Unfold the power of deep neural networks
✓ Improve Machine Learning algorithms: studying underfitting, overfitting, training, validation, & testing
✓ apply everything real-life situations
Carrier Based Learning Process
Course Curriculum (45 Hrs)
- Python Basics
- Python Data Structures
- Python Fundamentals
- Advance Python (oops)
- Pandas for Data Science
- NumPy for Data Science
- Matplotlib
- Seaborn
- Scikit-Learn
- NLTK
- Keras
- Tableau – Data visualization
- Tableau – Data Sources, Worksheet
- Introduction power BI
- Visualize Data in the Form of Various Charts, Plots, and Maps BI tools – Power BI
- Google Colab Notebook
- Python – Date and Time, Data Wrangling
- Python – Data Aggregation
- Python – Word Tokenization, Stemming, and Lemmatization
- Python – Data Visualization
- Python – Statistical Analysis
- Python – Types Of Distribution
- Python – Correlation, Chi-Square Test, Linear Regression
- Supervised Learning – Classification and Regression
- Liver disease Prediction – (Logistic Regression)
- Crime Analysis – (KNN Algorithm)
- Classifying muffins and cupcakes – (SVM Algorithm)
- Fake news detection – (Naïve Baye’s)
- Android malware – (Decision tree)
- Credit card Fraud detection – (Random Forest)
- Evaluating the classification model – (Confusion matrix) Calculating the accuracy score.
- Classification model selection For Breast Cancer – (Classification)
- Employee Salary Prediction – (Linear Regression Single Variable)
- Advertisement and Sales Prediction – (Multiple Linear Regression)
- Generating Data points based on some equation – (Polynomial Regression)
- Ice – Cream shop revenue prediction from temperature – (Decision Tree Regression)
- Agriculture Price Prediction – (Random Forest Algorithm)
- Evaluating the performance of my regression model – ( Root mean square error and R2 score)
- Regression Model selection for sales forecasting.
- Unsupervised Learning – Clustering
- Crime Pattern Analysis – (K-Means Clustering)
- Customer Spending Analysis – (Hierarchical Clustering)
- Flower Species – Data Visualization
- Image Compression Using – SVD (Singular Value Decomposition)
- Unsupervised Learning – Association
- Market Basket Analysis – (APRIARI)
- Market Basket Optimization / Analysis – (ECLAT)
- Reinforcement Learning
- Web Ads click-through rate optimization – (Upper Bound Confidence)
- Natural Language Processing
- Hate Speech Detection – (NLTK)
- Loan Prediction Problem – (XGBoost)
- Deep Learning
- Movie Review Classification – (RNN)
- Digits Classification – (CNN)
- Capstone – Analysis & Model Creation
- AI – Cart Pole (Reinforcement Learning)
Want to Become a
Data scientist?
The course provides the entire Knowledge you need to become a data scientist Fill up your resume with in-demand data science tools & skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and sci-kit-learn, Deep Learning with TensorFlow
Learning Essentials
Benefits of Learning Course
> Essential Python concepts for Data science
> Data types, variables, loops, and functions
> Data manipulation Llibraries NumPy and Pandas
> Visualize Data Llibraries: Matplotlib and Seaborn
> Visualize Data using Tools Tableau and Power bi
> Machine learning with Python Supervised, Unsupervised Reinforcement Learning
> Techniques and data analysis and Data storytelling
Trust of Worth
Pantech e Learning
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Expierience
Leaners from Pantech
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Students from all over india
Core Techologies
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Software - Electrical - Electronics -Automations
Advance Course Completion
Sample Certificate
Meet your Mentor
Sankara Venkat Ram V
The course provides the entire Knowledge you need to become a data scientist Fill up your resume with in-demand data science tools & skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and sci-kit-learn, Deep Learning with TensorFlow
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- 13 + Core Concepts (45 Hours total)
- Available from May, 26th
- Lifetime Access
- 1 Hrs Weekly Mentor Sessions
- Language: English
- Live Sessions with Mentor
What | How | Why?
Frequently Asked Questions
Data science is an interdisciplinary field that combines statistical analysis, computer science, and domain expertise to extract insights and knowledge from data.
Basic knowledge of statistics, programming, and mathematics is usually recommended.
The time it takes to learn data science can vary depending on your background and the amount of time you can devote to learning. Some courses offer a certificate after completing around 10-12 weeks of study, while others may take several months or even a year to complete.
Python and R are the two most commonly used programming languages in data science
While having a degree in data science or a related field can be helpful, it is not always necessary. Many data scientists come from diverse backgrounds such as computer science, mathematics, or statistics.
opular tools in data science include Python libraries such as Pandas, NumPy, and Scikit-learn, and data visualization tools such as Tableau and Power BI.
Common job titles in data science include data analyst, data scientist, machine learning engineer, and business intelligence analyst.
Earning potential for a data scientist can vary depending on location, industry, and level of experience. According to Glassdoor, the national average salary for a data scientist in the United States is around $113,000 per year.
To get started in data science, it’s recommended to begin by taking an online course, building a portfolio of projects, and networking with professionals in the field. It’s also important to continue learning and staying up-to-date with new technologies and trends.
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