Data Science with Big Data Training in Marathahalli  Data Science with Big Data Training in Bangalore

INTRODUCTION TO DATA SCIENCE

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

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

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

Hypothesis Testing

Introduction to Machine Learning
UNDERSTANDING AND IMPLEMENTING MACHINE LEARNING

Linear Regression
 Introduction to Linear Regression Linear Regression with Multiple Variables Copy
 Disadvantage of Linear Models Interpretation of Model Outputs Understanding Copy
 Multicolinearity Copy
 Missing & Outlier treatment Copy
 Understanding Heteroscedasticity Copy
 Case Study – Application of Linear Regression for CTG data Copy

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

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

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

1. Python Introduction (PART C – PYTHON PROGRAMMING)

2. Basics

3. Data Structures

4. Functions and Modules

Functional programming

6. File Handling and external integrations

7. 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
 • 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
 • 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

PART D – BIG DATA
1. Understanding Big Data and Hadoop

2. HDFS Architecture

3. Map Reduce

4. Advanced Map Reduce

5. Pig

6. Hive

7. HBase

8. Sqoop and Flume

9. Kafka

10. Oozie

11. Spark

12. Scala

Real Time Project

Resume Preparation Tips

Interview Guidance and Support
Data Science with Big Data 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)
learn about NLP training in Bangalore.,
Data science Training in Bangalore
Select Curriculum tab to see Data Science with Big Data Course Content
Course Features
 Lectures 223
 Quizzes 0
 Duration 60 Hours
 Skill level All level
 Language English
 Students 10
 Assessments Yes
5 Comments
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