Data Science with Python Training in Marathahalli  Data Science with Python Training in Bangalore

INTRODUCTION TO DATA SCIENCE

Fundamentals of Math and Probability
INTRODUCTION TO STATISTICS
 Basic understanding of linear algebra, Matrices, vectors
 Addition and Multiplication of matrices
 Fundamentals of Probability
 Probability distributed function and cumulative distributed function
 Conditional Probability
 Class Handon – Problem solving using R for vector manipulation Problem solving for probability assignments Copy Copy

Descriptive Statistics
 Describe or summaries a set of data Measure of central tendency and measure of dispersion.
 The mean, median, mode, Standard deviation, Variance, Range, kurtosis and skewness.
 Histograms, Bar chart, Box plot
 Class Handson 5 Point summary Box Plot, Histogram and Bar Chart Exploratory analytics R Methods Copy Copy

Inferential Statistics
 What is inferential statistics Different types of Sampling techniques Central Limit Theorem
 Univariate & Bivariate Analysis
 Correlations
 Least Square Regression
 Normal Distribution
 Binomial Distribution & Quincunx
 Point estimate and Interval estimate
 Creating confidence interval for population parameter Characteristics of Z distribution and TDistribution Basics of Hypothesis Testing Copy Copy
 Bias & Variance tradeoffs
 Type of test and rejection region
 Type of errors in Hypothesis resting, Typel error and Typell errors
 False Positive & False Negative
 PValue and ZScore Method
 TTest, Analysis of variance(ANOVA) and Analysis of Co variance(ANCOVA)
 Regression analysis in ANOVA
 Problem solving for C.L.T Problem solving Hypothesis Testing Problem solving for Ttest, Zscore test Copy Copy
 Case study and model run for ANOVA, ANCOVA

Hypothesis Testing

Introduction to Machine Learning
UNDERSTANDING AND IMPLEMENTING MACHINE LEARNING

Linear Regression

Logistic Regression
 Introduction to Logistic Regression
 Binary Logistic Regression
 Multinomial Logistic Regression
 Introduce the notion of classification Cost function for logistic regressio
 Application of logistic regression to multiclass classification.
 Confusion Matrix, Odd's Ratio and ROC Curve Advantages and Disadvantages of Logistic Regression Copy Copy
 AIC & BIC

Decision Trees
 Decision Tree – C4.5, CART, CHAID
 How to build decision tree? Understanding CART Model Classification Rules
 Overfitting Problem Stopping Criteria And Pruning
 Underfitting
 Gini Index
 Informations Gain
 How to find final size of Trees? Model A decision Tree.
 MDS Copy
 Random Forests and Support Vector Machines Interpretation of Model Outputs
 Case Study – 1 Business Case Study for Kart Model

Unsupervised Learning
 Feature Selection & Feature Extraction
 Feature Construction
 Hierarchical Clustering
 KMeans algorithm for clustering – groupings of unlabeled data points.
 Principal Component Analysis(PCA)
 Anomaly Detection
 Association rules
 Market Basket Analysis
 Customer Segmentation
 Dimensionality reduction on CTG

PART C

PYTHON PROGRAMMING

Basics

Data Structures

Functions and Modules

Functional programming

File Handling and external integrations

Python for Data Science
 • Numerical Python
 a) nd array b) Subset, slicing c) Indexing d) List vs nd array e) Manipulating arrays f) Mathematical operations and apply functions g) Linear algebra operations Copy
 • Pandas
 a) Data loading b) Series and Data frame c) Selecting rows and columns d) Position and labelbased indexing e) Slicing and dicing f) Merging and concatenating g) Grouping and summarizing h) Lambda functions and pivot tables i) Data Processing, cleaning j) Missing Values k) Outliers Copy
 • Data visualization
 a) Introduction to Matplotlib Basic plotting Figures and sub plotting Box plot, Histograms, Scatter plots, image loading b) Introduction to Seaborn Histogram, rugged plot, hex plot and density plot Joint plot, pair plot, count plot, Heatmaps c) Plotting categorical data and aggregation of values d) Plotting TimeSeries data using tsplot Copy

Real Time Project

Resume Preparation Tips

Interview Guidance and Support
Data Science with Python Training in Marathahalli Bangalore.
What is Data Science with big data?
Eminent IT is the best Institute to learn Data science course in Marathahalli Bangalore. Data Science is a multidisciplinary field that uses data, algorithms, and scientific methods. To obtain insights from both unstructured and structured realtime data.
Data Science Course in Bangalore
According to Harvard Business Review called it “Coolest job of the 21st Century”. making it one of the sought after position in IT field around the world.
It is important for any new business to gain the insights both large and small. Based on user preference and choices, making sense of this huge data is a complex and timeconsuming task. With help of data science powered by AI and ML, this process is simplified to give the results that are accurate and scalable for realtime insights.
BENEFITS OF TAKING THE MACHINE LEARNING WITH BIG DATA & PYTHON COURSE
 Learn to analyze data using machine learning techniques in Python
 Learn how Machine learning models are deployed in Big Data environment
 Become one of the most indemand machine learning experts in the world today
 Learn how to analyze large amounts of data to bring out insights
 Relevant examples and cases make the learning more effective and easier
 Gain handson knowledge through the problem solving based approach of the course along with working on a project at the end of the course
WHO SHOULD Take this Course ?
This course is designed for anyone who:
 wants to get into a career in Data Science & Machine Learning
 wants to analyze large amounts of data to bring out the insights from the same
 wants to learn Python for working on machine learning projects
 wants to automate decision making and create webbased machine learning applications
PREREQUISITES
 Ideally, you should be familiar with some programming (in any language).
 The course assumes a working knowledge of key data science topics (statistics, machine learning, and general data analytic methods). Programming experience in some languages such as R, Python, etc., is expected. In particular, participants need to be comfortable with general programming concepts like variables, loops, and functions. Experience with R or Python is helpful (but not required)
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Data science Training in Bangalore
Select Curriculum tab to see Data Science with Big Data Course Content
Course Features
 Lectures 114
 Quizzes 0
 Skill level All level
 Language English
 Students 10
 Assessments Yes