Data Science COURSE

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Data Science Course Overview

Data Science Training in Bangalore

MNP Technologies located in Marathalli, Bangalore is a leading training institute providing real-time and placement oriented Data Science Training Courses in Bangalore. Our Data Science training course includes basic to advanced levels. we have a team of certified trainers who are working professionals with hands on real time Data Science projects knowledge which will provide you an edge over other training institutes.

Our Data Science training center is well equipped with lab facilities and excellent infrastructure for providing you real time training experience. We also provide certification training programs in Data Science Training. We have successfully trained and provided placement for many of our students in major MNC Companies, after successful completion of the course. We provide placement support for our students.

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Data Science Course Duration & Timings

Our team of experts at MNP Technologies Training Institute, Bangalore have designed our Data Science Training course content and syllabus based on students requirements to achieve everyone’s career goal. Our Data Science Training course fee is economical and tailor-made based on training requirement.

We Provide regular training classes(day time classes), weekend training classes, and fast track training classes for Data Science Training in our centers located across Bangalore. We also provide Online Training Classes for Data Science Training Course.

Data Science Course Syllabus

Part – 1

Introduction to data science. Various data science disciplines
The field of data science. Connecting the data science disciplines
The benefits of each discipline
Popular data science techniques
Popular data science tools
Careers in data science
Debunking common misconceptions

Part 2: Bayesian Inference

Sets and events
Ways sets can interact
Union and intersection of sets
Mutually exclusive sets
Dependence and Independence sets
The conditional probability formula
The law of total probability
The additive rule
The multiplicative law
Bayes law
Practical example of bayes law

Part – 2

Probability: Basic probability formula
Computing expected value
Frequency
Events and their complements
Quiz and assignment

Probability distributions

Fundamentals of probability distributions
Types of probability distributions
Characteristics of discrete distributions
Uniform distribution
Binomial distribution
Poisson distribution
Characteristics of continuous distribution
The normal distribution
The standard normal distribution
The students T distribution
The chi square distribution
The exponential distribution
The logistic distribution
A practical example of probability distribution

Probability – Combinators

Fundamentals of Combinatorics
Permutations and how to use them
Simple operations with factorials
Solving variations with repetition
Solving combinations
Symmetry of combinations
Solving combinations with separate sample spaces
A practical example of combinatorics

Probability in other fields

Probability in finance
Probability in Statistics
Probability in data science

Descriptive statistics

Types of data
Levels of measurement
Categorical variable visualization techniques
Numerical variable – Frequency distribution
Histogram
Cross table and scatter plot
Mean, median and mode
Skewness and Kurtosis
Variance, standard deviation and coefficient of variation
Covariance and correlation coefficient
Percentile, Quartile and decile
Assignments

Inferential statistics

Central limit theorem
Estimators and estimates
Hypothesis testing
Type one and type two error
Level of significance
P value
Confidence interval

Introduction to Python

Why Python and Jupyter
Installing Anaconda in windows
Python variables and data types
Basic python syntax
Strings and numbers
For and while loop
IF and ELIF statements
Arithmetic operators
The double equality sign
Python user defined functions
Logical operators
Indexing elements
Continue and break statement
Comparison operators
File input and output with python
Error handling with python
Python sequences
Object oriented programming basics

Data Analysis with Pandas

Import and export data
Group by function
Pivot table
Crosstabulation
Plot function
Charting with pandas

Data visualization with matplotlib and seaborn

Line chart
Histogram
Area chart
Pie chart
Heatmap
Scatter plot
Bar chart

Statistical modelling with Python

Linear regression
Logistic regression
Decision tree
Support vector machine
Naïve bayes classifier
K nearest neighbor classifier
Ensemble learning models
Random forest classifier
Boosting classifier
Cluster Analysis
Case studies and Assignments

Key Benefits

Discover Our Unique Learning Advantages

Real Time Trainers

Learn from industry experts with hands-on experience in real-time projects and current technologies.

100% Placement Assistance

Get end-to-end support with resume building, interview preparation, and job referrals to top companies.

Flexible Timings

Choose from weekday or weekend batches to suit your schedule — ideal for students and working professionals.

Excellent Lab Facility

Access modern, fully-equipped labs to practice and perfect your skills during and after sessions.

Practical Guidance

Move beyond theory with practical insights, real-world case studies, and mentor-led guidance.

Hands-on Experience

Work on live projects and real-time assignments to gain confidence and job-ready expertise.

Certificate Support

Receive course completion certificates and guidance for global certification exams in your domain.

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