Data science is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract insights and knowledge from structured and unstructured data. For More Details Visit: https://datamites.com/data-science-course-training-pune/

1. DataMites™
Data Science Course in Pune
Upcoming Batch Schedules – January 2024
Toll Free Number: 1800 313 3434

2. What Is Data Science
Data science is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to
extract insights and knowledge from structured and unstructured data. It combines techniques from statistics,
mathematics, computer science, and domain-specific knowledge to analyze and interpret complex data sets. The
goal of data science is to uncover hidden patterns, trends, and valuable information that can be used to make
data-driven decisions and predictions.
Key components of data science include:
• Data Collection: Gathering relevant and meaningful data from various sources, which can include databases,
APIs, sensors, and more.
• Data Cleaning and Preprocessing: Ensuring that the collected data is accurate, complete, and ready for
analysis by addressing issues such as missing values, outliers, and inconsistencies.
• Exploratory Data Analysis (EDA): Analyzing and visualizing data to understand its characteristics, patterns,
and relationships. EDA helps in identifying potential insights and formulating hypotheses.
• Feature Engineering: Creating new features or modifying existing ones to improve the performance of
machine learning models.
• Model Development: Building and training machine learning models to make predictions or classifications
based on the data.

3. What are the technical skills required to
become a Data Scientist
Becoming a data scientist requires a combination of technical skills from various domains. Here's a list of key technical skills
commonly associated with the field of data science:
Programming Skills:
Python or R: These are the most popular programming languages for data science. Python, in particular, is widely used for its
versatility, extensive libraries (e.g., NumPy, Pandas, Scikit-Learn), and ease of learning.
Statistical Skills:
Statistical Knowledge: Understanding statistical concepts and methods is crucial for data analysis and model building. This
includes probability, hypothesis testing, regression analysis, and more.
Data Manipulation and Analysis:
Pandas: A powerful Python library for data manipulation and analysis. It provides data structures like DataFrames that are essential
for working with structured data.
SQL: Proficiency in SQL is important for extracting and manipulating data from relational databases.
Data Visualization:
Matplotlib, Seaborn, Plotly: Tools for creating visualizations in Python.
ggplot2 (in R): A popular R package for creating data visualizations.
Machine Learning:
Scikit-Learn: A machine learning library for Python that provides simple and efficient tools for data analysis and modeling.
TensorFlow or PyTorch: Deep learning frameworks for building and training neural networks.

4. Upcoming Batches in Pune
• Training Dates:
8th January 2024 ( Online – Weekday)
20th January 2024 ( Online – Weekend)
22nd January 2024 ( Online – Weekday)
27th January 2024 (Classroom – Weekend)
22nd January 2024 ( Online – Weekday)
15th January 2024 ( Classroom – Weekday)
29th January 2024 ( Online – Weekday)
Course Details
• 8 Months Course
• Project work
• IABAC Certification
• Internship Project

5. What are the business skills required
to become a Data Scientist
Communication Skills:
Data Storytelling: Ability to communicate complex technical findings in a clear and compelling manner to non-technical
stakeholders.
Visualization: Proficiency in creating visualizations that effectively convey insights to diverse audiences.
Problem-Solving:
Critical Thinking: The capacity to approach problems analytically and think critically about data and its implications for business
decisions.
Creative Problem-Solving: Finding innovative solutions to business challenges using data-driven approaches.
Domain Knowledge:
Industry Understanding: Familiarity with the specific industry or business domain in which you work is crucial for framing
relevant questions and providing actionable insights.
Business Acumen:
Understanding of Business Processes: Knowledge of how different business functions operate and how data science can contribute
to achieving business objectives.
Collaboration and Teamwork:
Interdisciplinary Collaboration: Ability to work effectively with professionals from diverse backgrounds, such as business
analysts, managers, and subject matter experts.
Teamwork: Contributing to and collaborating with cross-functional teams to solve complex problems.

6. Datamites™
THANK YOU
If you are looking for a Data Science course in Pune,
please visit
https://datamites.com/data-science-course-training-pune/