# Data Science with Python Training in Marathahalli | Data Science with Python Training in Bangalore Copy

#### 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 Hand-on – 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 Hands-on- 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 T-Distribution Basics of Hypothesis Testing Copy Copy
- Bias & Variance trade-offs
- Type of test and rejection region
- Type of errors in Hypothesis resting, Type-l error and Type-ll errors
- False Positive & False Negative
- P-Value and Z-Score Method
- T-Test, 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 T-test, Z-score 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 multi-class 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
- K-Means 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

#### 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 Copy
- • 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 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 Time-Series 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 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 114
- Quizzes 0
- Duration 60 Hours
- Skill level All level
- Language English
- Students 10
- Assessments Yes