Key Concepts in Data Science in India | IABAC

Key concepts in data science in India include data wrangling, statistical analysis, machine learning, big data technologies, and AI integration. Focus areas are predictive modeling, data-driven decision-making, and the ethical use of data in various sectors.

1. Key Concepts in
Data Science in
India
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2. Content
1. Fundamental Statistical Concepts
2. Core Data Science Techniques
3. Application of Data Science in India
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3. 01 02 03
Probability Basics
Sampling
Techniques
Distribution of
Data
Understanding probability
is crucial in data science
to analyze uncertain
events and make
informed decisions.
Learning about population
and sample helps in
drawing meaningful
inferences from data.
Understanding the
distribution of data aids in
identifying patterns and
making predictions.
Fundamental Statistical Concepts
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4. Measures of Central Tendency
Mean, Median, and Mode
Standard Deviation
Skewness and KurtosisThese measures help in understanding the central values of a dataset and are essential in data analysis. Knowing the spread of data around the mean is vital for assessing the variability in a dataset. These concepts provide insights into the shape and symmetry of data distributions.
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5. 01 02 03
Core Data Science Techniques
Data Extraction Data
Transformation
Predictive
Analytics
Data mining techniques
involve extracting patterns
from large datasets to
uncover valuable insights.
Data engineering focuses
on preparing and
transforming raw data into
a usable format for
analysis.
Utilizing historical data to
predict future trends and
behavior is a key aspect
of data science.
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6. Machine Learning and Cloud Computing
Supervised Learning
Unsupervised Learning
Cloud InfrastructureUnderstanding how machine learning algorithms learn from labeled data to make predictions is crucial. Exploring unsupervised learning techniques for clustering and dimensionality reduction is essential in
data science. Leveraging cloud computing for data storage and processing is integral for scalable data science
projects.
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7. Data Visualization and Interpretation
Visual Representation Interpreting Results Dashboard Creation
Creating effective visualizations
aids in communicating insights
and patterns within data.
Learning to interpret visualized
data helps in drawing meaningful
conclusions and making data-
driven decisions.
Developing interactive dashboards
for data exploration and analysis
enhances the understanding of
complex datasets.
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8. 01 02 03
Financial Services Retail and E-
commerce Healthcare and
Agriculture
Exploring how data
science is used in
banking, insurance, and
investment analysis in the
Indian context.
Understanding the
application of data
science in customer
segmentation, demand
forecasting, and
personalized marketing.
Exploring the impact of
data science on medical
diagnostics, disease
prediction, and precision
agriculture in India.
Application of Data Science in India
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9. Job Growth and Opportunities
Data Science Careers
Skill Development
IABACDiscussing the growing demand for data scientists and the diverse career opportunities available in
India. Highlighting the importance of acquiring data science skills and the potential for career growth in the
field. Introducing the International Association of Business Analytics Certifications (IABAC) and its role in
promoting data science education and certification in India.
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10. Thank you
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