Online data science courses in Kollam
Our Data Science Course is meticulously crafted to empower you with the skills and insights needed to navigate the complex landscape of data-driven decision-making. Whether you are an aspiring data scientist or a professional looking to enhance your analytical prowess, our comprehensive program caters to all levels of expertise. The role of a Data Scientist necessitates a combination of practical experience, comprehensive knowledge in Data Science, and proficiency in utilizing appropriate tools and technologies. This career path is a robust option suitable for individuals at various career stages, including both novices and seasoned professionals. Those aspiring to enter this field, regardless of their educational background, will find the Data Scientist Master’s Program particularly well-suited, especially if they possess an analytical mindset.
Immerse yourself in a comprehensive training experience that focuses on the highly sought-after skills in Data Science and Machine Learning. Gain practical insights into essential tools and technologies, such as Python, R, Tableau, and the core concepts of Machine Learning.Transform into a Data Science expert as you explore the intricacies of data interpretation, master cutting-edge technologies like Machine Learning, and refine your programming skills. Elevate your career in Data Science to new heights through this dynamic program.
About the Course
Day 1: Introduction to Data Science Learning
Objective: Understand what data science is and its applications. What is Data Science? Introduction to Data Science in Python
Day 2: Introduction to Statistics Learning
Objective: Understand the basics of statistics and its role in data science. Introduction to Probability and Statistics Statistics Fundamentals
Day 3: Introduction to Python Learning
Objective: Learn the basics of Python programming language. Python for Everybody Introduction to Python
Day 4: Data Wrangling Learning
Objective: Learn how to clean and prepare data for analysis. Data Wrangling with Python Data Cleaning with Python
Day 5: Data Visualization Learning
Objective: Learn how to create visualizations and gain insights from data. Data Visualization with Python Data Visualization in Python
Day 6: Machine Learning Fundamentals Learning
Objective: Learn the basics of machine learning and how it’s used in data science. Introduction to Machine Learning Machine Learning Fundamentals
Day 7: Exploratory Data Analysis Learning
Objective: Learn how to analyze data and identify patterns. Exploratory Data Analysis in Python Data Science Handbook
Day 8: Supervised Learning
Objective: Learn how to use supervised learning to make predictions. Supervised Learning with Python Machine Learning Mastery
Day 9: Unsupervised Learning Learning
Objective: Learn how to use unsupervised learning to identify patterns in data. Unsupervised Learning with Python Clustering with Scikit-Learn
Day 10: Data Ethics and Privacy Learning
Objective: Understand the ethical considerations in data science and privacy concerns. Data Ethics Data Privacy
Online data science courses in Kollam
Day 11: Linear Regression Learning
Objective: Learn how to use linear regression to make predictions. Linear Regression Introduction to Linear Regression Analysis
Day 12: Logistic Regression Learning
Objective: Learn how to use logistic regression to make binary predictions.
Day 13: Decision Trees Learning
Objective: Learn how to use decision trees to make predictions.
Day 14: Random Forests Learning
Objective: Learn how to use random forests to make predictions.
Day 15: Neural Networks Learning
Objective: Learn how to use neural networks to make predictions.
Day 16: Evaluation Metrics Learning
Objective: Learn how to evaluate the performance of machine learning models.
Day 17: Feature Engineering Learning
Objective: Learn how to select and engineer features for machine learning models.
Day 18: Machine Learning Algorithms Learning
Objective: Understand machine learning algorithms and their applications
Day 19: Deep Learning Learning
Objective: Understand deep learning and its applications, Deep Learning Introduction Lesson, Artificial Neural Network Lesson, Deep Neural Network and Tools Lesson , Tuning, and Interpretability
Day 20: Convolutional Neural Networks (CNN) Recurrent Neural Networks Autoencoders
Day 21: Data Visualization & Web Scraping Learning
Objective: Learn how to scrape data from websites Web Scraping with Python Beautiful Soup Scrapy
Day 22: Natural Language Processing Learning
Objective: Learn how to process and analyze natural language data Natural Language Processing with Python by NLTK Spacy Tutorial
Day 23: Data Science Tools Learning
Objective: Learn how to use various tools for data science Anaconda Navigator Tutorial Git and GitHub Jupyter Notebook
Day 24: Data Wrangling Learning
Objective: Learn how to clean and manipulate data Data Wrangling with Pandas Pandas Documentation Data Wrangling with Python
Day 25: Exploratory Data Analysis Learning
Objective: Learn how to explore and analyze data Exploratory Data Analysis with Pandas Seaborn Tutorial
Day 26–30: Capstone Project Learning
Objective: Apply all the concepts learned to complete a real-world
Tools Covered
Projects
BUILDING A USER BASED RECOMMENDATION MODEL FOR AMAZON
The data set provided contains movie reviews given by Amazon customers. Perform data analysis on the Amazon customer movie reviews data set and build a Machine Learning recommendation algorithm which provides the ratings for each of the users.
RETAIL ANALYSIS WITH WALMART
One of the leading retail stores in the US, Walmart, would like to predict sales and demand accurately. The business is facing a challenge due to unforeseen demands and runs out of stock occasionally. It’s discovered that a Machine Learning algorithm is at the core of this issue. Build an ideal ML algorithm that will predict demand accurately and incorporate factors like economic conditions including CPI, unemployment index, etc.
CUSTOMER SERVICE REQUESTS ANALYSIS
Perform data analysis on New York City 311 service request calls. You will focus on data wrangling techniques to understand data patterns and also create visualizations to categorize and prioritize complaint types, like economic conditions including CPI, Unemployment Index, etc. Mercedes-Benz’s standards.
COMPARATIVE STUDY OF COUNTRIES
One of the leading retail stores in the US, Walmart, would like to predict sales and demand accurately. The business is facing a challenge due to unforeseen demands and runs out of stock occasionally. It’s discovered that a Machine Learning algorithm is at the core of this issue. Build an ideal ML algorithm that will predict demand accurately and incorporate factors like economic conditions including CPI, unemployment index etc
SALES PERFORMANCE ANALYSIS
Build a dashboard that will present monthly sales performance by product segment and product category to help clients identify the segments and categories that have met or exceeded their sales targets, as well as those that have not met their sales targets.
PREDICT THE DEMAND OF LOAN BASED ON REGION
This project provides learners with insights into the banking sector. Learners are required to build a statistical model to predict the demand for loans in a particular region. To show the results, learners are required to provide an online dashboard that shows the plan and its progress to all stakeholders.
BUILD MODEL TO PREDICT DIABETIC PATIENTS
The project is aligned with NIDDK (National Institute of Diabetes and Digestive and Kidney Diseases) data sets representing one of the most chronic and consequential diseases. The goal of this project is to build a model to predict the patients with diabetes by utilizing the given data set.
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