# 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 Hand-on – 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 Hands-on- 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 T-Distribution Basics of Hypothesis Testing Copy
- Bias & Variance trade-offs Copy
- Type of test and rejection region Copy
- Type of errors in Hypothesis resting, Type-l error and Type-ll errors Copy
- False Positive & False Negative Copy
- P-Value and Z-Score Method Copy
- T-Test, 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 T-test, Z-score 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
- Multi-colinearity 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 multi-class 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
- K-Means 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 label-based 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 Time-Series 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 multi-disciplinary field that uses data, algorithms, and scientific methods. To obtain insights from both unstructured and structured real-time 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 time-consuming 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 real-time 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 in-demand 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 hands-on 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 web-based machine learning applications

**PRE-REQUISITES**

- 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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