Data Science Training in Chandigarh
                     Future of 
Data 
Science
What is Data 
Science?
• Definition: An interdisciplinary field focused 
on extracting knowledge and insights from 
data.
• Combines:  Statistics, programming, machine 
learning, and domain expertise
• Data Scientists:  Professionals who use tools 
and techniques to find patterns and value 
within data.
• Applications:  Business analytics, healthcare, 
scientific research, and more.
Begin by establishing a clear understanding of 
data science. Emphasize its interdisciplinary 
nature, highlighting how it combines technical 
and analytical skills. Mention real-world 
applications to spark curiosity.
Early Foundations
• 1960s: John Tukey, a visionary in statistics, introduced the term "data analysis" – a 
field strongly resembling modern data science.
• The Rise of Computers: The development of computer technology offered 
unprecedented power to store and analyze data
• Origins of Machine Learning: Early exploration of basic machine learning concepts 
during this time laid groundwork for future advancements.
The groundwork for data science was established as early as the 1960s. Tukey's 
championing of "data analysis"  helped crystallize concepts that are core to data 
science today.  This era also witnessed the increasing capabilities of computers 
which became vital tools, alongside the early stages of machine learning research.
Why Study Data 
Science?
• High Demand:  Rapid growth of data fuels the 
need for skilled data scientists.
• Excellent Salaries: Data science consistently 
ranks among the top-paying tech careers.
• Diverse Opportunities: Work across various 
industries and sectors.
• Problem-Solving: Address complex challenges 
using data-driven approaches.
• Impactful Work: Shape innovative solutions 
and drive decision-making.
Stress the strong job market and competitive 
compensation for data scientists. Highlight the 
versatility and problem-solving aspects of the 
role, emphasizing its potential to make a real-
world difference.
Skills You'll Learn
• Programming: Python and/or R
• Statistics: Fundamental concepts and 
techniques
• Machine Learning: Algorithms for prediction 
and classification
• Data Visualization:  Creating compelling charts 
and graphs
• Data Wrangling: Cleaning and preparing data
• Big Data Tools:  Hadoop, Spark (if applicable)
Provide a clear list of core data science skills 
covered in typical training programs. Maintain a 
balance between hard skills like programming 
and analytical concepts like statistics.
The Data Science Process
Problem Definition: Clearly define the business or research question.
Data Collection: Gather relevant data from various sources.
Data Cleaning: Address inconsistencies, errors, and missing values.
Exploratory Analysis: Visualize and understand patterns in the data.
Modeling: Build machine learning models to predict or classify.
Evaluation:  Assess model performance using relevant metrics.
Deployment & Communication: Share insights and implement solutions.
Types of Data
Structured Data: Easily organized in tables (e.g., customer records)
Semi-structured Data:  Has some organization, but not rigid (e.g., 
JSON, XML)
Unstructured Data: No inherent structure (e.g., text, images, videos)
• Dealing with missing values
Data • Correcting errors
Cleaning • Transforming data formats
• Feature engineering (creating new 
and features)
Preparatio Data cleaning is often the most time-consuming part of a data science project. 
n But it's crucial! The saying "garbage in, 
garbage out"  holds true –  flawed data 
leads to flawed results.
Exploratory Data Analysis (EDA)
VISUALIZATIONS (HISTOGRAMS, SUMMARY STATISTICS (MEAN, CORRELATION ANALYSIS EDA IS LIKE DETECTIVE WORK.  
SCATTER PLOTS, BOX PLOTS) MEDIAN, STANDARD DEVIATION) YOU'RE LOOKING FOR PATTERNS, 
ANOMALIES, RELATIONSHIPS – 
ANYTHING THAT HELPS INFORM 
YOUR ANALYSIS OR RAISES NEW 
QUESTIONS TO EXPLORE.
Machine Learning
What is it? Algorithms that learn patterns from data 
without explicit programming.
Types:
• Supervised Learning (predict labels/values)
• Unsupervised Learning (find clusters/ patterns)
• Reinforcement Learning (learning through trial and 
error)
• Popular Algorithms:  Linear regression, decision 
trees, support vector machines, neural networks
Machine learning is the core of making predictions or 
classifications from data. Supervised learning works 
with labeled data, unsupervised looks for hidden 
structure, and reinforcement learning is how 
machines learn to play games like chess.  Many 
algorithms exist, and choosing the right one depends 
on your problem.
Feature Engineering
• Scaling and normalization
• Dimensionality reduction (PCA, etc.)
• Feature selection
• Generating new features (e.g., 
interactions)
The quality of your features often 
determines how well your models 
perform.  Feature engineering is where 
domain knowledge is especially important 
- understanding your data helps you craft 
features that will best capture the 
underlying relationships.
Model Evaluation
Choosing the Right Metrics:  Regression (e.g., R-squared, Mean Squared Error) vs. classification 
(e.g., accuracy, precision, recall)
Cross-Validation: Preventing overfitting by evaluating models on unseen data.
Hyperparameter Tuning: Finding the best settings for your models.
Not all models are created equal! We use metrics to pick the best model and ensure it will 
generalize to new data it hasn't been trained on. Hyperparameters are like knobs we can tweak 
to optimize a machine learning algorithm's performance.
Data 
Visualization
• Telling Stories with Data:   
Communicating insights visually.
• Choosing the Right Charts:  Line charts 
for trends, bar charts for comparison, 
scatter plots for relationships, etc.
• Tools: Tableau, PowerBI, Python/R 
libraries (Seaborn, ggplot2)
A good visualization is worth a thousand 
words!  Choose the right type of chart to 
clearly show the point you're making.  
Many great tools exist, from powerful 
business intelligence platforms to coding 
libraries that allow for fine-grained control.
• Business:  Customer analytics, fraud detection, 
targeted marketing
• Healthcare:  Disease diagnosis, drug discovery, 
personalized medicine
• Science:  Analyzing experimental data, modeling 
complex systems
• And Many More: Finance, transportation, social 
media... the list goes on!
Data Science Data science applications are all around us! 
Applications Businesses use it to make better decisions, healthcare uses it to improve patient outcomes, 
scientists use it to unlock new discoveries. The 
possibilities are nearly endless in any field that 
generates data!
Data science is not merely a tool or 
technology; it's a transformative force that 
revolutionizes how organizations operate, 
Conclusio innovate, and compete in today's data-driven 
world. Embracing data science opens doors to 
n unprecedented opportunities for growth, 
efficiency, and impact, making it an 
indispensable asset in the modern business 
landscape.
Thank You !
Data Science Training in Chandigarh
For Query Contact : 998874-1983