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.

         Contact us today to schedule a free demo and complete course details on 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

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        Frequently Asked Questions

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