Data Science Training in Pune

The field of Data Science integrates statistics, computer science, and domain expertise to extract insights from data. It is a relatively young field that arose in response to the growing availability of enormous data sets and the demand for more effective evaluation methods.
Data Science has numerous facets, but data manipulation, exploratory data analysis, predictive modeling, and machine learning are among the most essential.
Data Science is a potent field used for comprehending the world and making more informed judgments. It can enhance enterprises, create superior goods, and resolve complex issues.

Data scientists utilize their expertise to tackle complicated challenges and assist organizations in making more informed decisions. Most data scientists have a bachelor’s degree or higher in a quantitative subject, such as mathematics, statistics, or computer science. Numerous possess a master’s degree or above. In addition to their academic credentials, data scientists must possess strong analytical and problem-solving abilities. They must be able to clearly and effectively express complicated ideas.

In many instances, choices inside businesses are made more based on a gut feeling than facts and statistics. One of the possible explanations for this is that there is insufficient access to high-quality data that may facilitate improved decision-making.

For executives to be able to make more informed decisions, analytics may assist in the process of translating the data that is already accessible into usable information. If fewer poor judgments are made, this may be a source of competitive advantage for the firm because poor decisions can negatively influence several areas, including the growth of the company and its profitability.

Customers in today’s society have a wide variety of options to select from. It is possible for businesses to quickly find themselves in a downward spiral if they are not attuned to the wants and expectations of their customers. Customers have the propensity to alter their opinions due to the constant barrage of fresh information they are subjected to in this era of digitalization.
Data Science Job Roles and Salary

The field of Data Science encompasses a wide variety of various employment functions and responsibilities. Here are four of the most prevalent job in the field of Data Science:

Data Scientist: Data Scientists are responsible for gleaning meaningful insights from large amounts of data. Utilizing methods from statistical analysis, machine learning, and artificial intelligence is required for this task. Most of the time, those who work in Data Science have a background in mathematics, statistics, or computer science. A Data Scientist can make up to 10 Lakhs Indian Rupees per year on average (INR).

Data Engineer: These engineers are tasked with the responsibility of constructing and maintaining the systems that are utilized by data scientists in the course of their work. Among these tasks are the creation of algorithms, the construction of databases, and the improvement of data pipelines. Those who work in data engineering often have a background in engineering or computer science. A Data Engineer can make up to 10 Lakhs (INR) annually on average.

Business Analyst: They are tasked with improving business decision-making by using the available data better. This requires seeing patterns, developing hypotheses, and experimenting with experimental plans. Business analysts often come from financial or business-related educational backgrounds. A Business Analyst can make up to 8 Lakh Indian Rupees annually (INR).

Product Manager: These professionals are accountable for making decisions about their products based on the collected and analyzed data. This requires the establishment of goals, the review of the evidence, and the formulation of suggestions. Product managers often have prior experience in either the business or engineering fields. The average salary for a Product Manager is up to 10 Lakhs Indian Rupees (INR).

Syllabus

Introduction to Data Science
Python for Data Science
• Introduction to Python Programming
• Programming in Python
• Python for Data Science
• Data Visualization in Python
Statistics
• Introduction to Statistics for DS
• Descriptive Statistics
• Inferential Statistics
• Hypothesis Testing
• Types of Hypothesis Testing
• Fundamentals of Regression Analysis
Excel
• Excel With Formulas
• Conditional Statements
• Formula Based Conditional Formatting
• Data Visualization
• Excel Chart Formatting
• Formatting Data with Pivot Tables
Getting Started with SQL
• Introduction to SQL
• SQL Constraints and Alias
• SQL Selection Commands
• SQL Functions
• SQL Joins
• SQL Views
• SQL Indexes
Analyzing Data with Python
• Python Packages
• Numpy, Pandas, Scipy, Matplolib, Seaborn
• Types of Data Analytics
Machine Learning
• Introduction to Machine Learning
• Types of Algorithms
• Supervised, Unsupervised, Reinforcement Learning
• Regression, Linear Regression
• Clustering
• Classification
• Decision Tree, Random Forest, Boosting
• Model Selection, Random Search,
• Time Series, Handling Time Series in Pandas
Machine Learning
• Introduction to Machine Learning
• Types of Algorithms
• Supervised, Unsupervised, Reinforcement Learning
• Regression, Linear Regression
• Clustering
• Classification
• Decision Tree, Random Forest, Boosting
• Model Selection, Random Search,
• Time Series, Handling Time Series in Pandas
NLP
• Introduction to NLP
• Application of NLP