Data Science Courses in Marathahalli, Bangalore
Data Science Courses in Marathahalli,bangalore
Data Science has become one of the most popular career options among young students. This branch of study consists of the learning and training about analytical tools like SAS, R, Python, Tableau and many more. It requires the knowledge about statistics, computer application techniques, mathematics and other incorporate studies like cluster analysis, data mining, machine learning etc.Learn Data Science Courses in Marathahalli.
Data Science course is necessary for all the individuals who want to pursue their career as a Data Scientist and Big Data specialist. It not only empowers the expertise of skills of the candidates but also prepares them to study the most technical components in the IT industry. There are a plethora of training courses available in order to sharpen your skill requirement and enhance your productivity level for the organizations.
Data Science Courses in Marathahalli offers excellent training programs and courses in order to enhance the efficiency and skill level of the candidates who want to become Data Scientist. These executives play a prominent role in increasing the productivity level and profit margins of the companies. They not only provide them the quantifiable information through IoT devices and data analytics but also facilitates in decision-making process.
Advantages of Data Science Courses in Marathahalli:
- Learn to overcome the mistakes in Data Science projects.
- Understand new areas of diversity and updates in the field of Big Data and Data Science.
- Enhance your skills by the team of top most experienced professionals.
- Work on live projects and assignments and understand the tactics of grabbing opportunities in Data Science management.
- Learn to handle and mitigate the risk factor in Data Science.
- Get placement assistance and guidance.
- Learn about the non-technical skills like leadership, networking, communication, teamwork and many more.
- Get high paid salary packages in the topmost leading companies and IT industries like Cisco, Intel, Google, Dell etc.
Features of Data Science Training in Marathahalli:
- The training institutes offer full-time course material in order to give theoretical knowledge to the individuals and improve their basics.
- There is a 24/7 lab facility for the students so that they can work on live projects and assignments for enhancing their analytical skills.
- These training included 60 hours of training classes along with the benefit of backup classes for those who want to learn from online sessions.
- Data Science Courses in Marathahalli involves a team of experienced professionals who have been teaching for more than 9 years.
- These institutes also assure you short period internship programs in order to improve your abilities and capabilities of Data Science and to provide you with a glimpse of working in a real-time work environment.
Data Science Courses in Marathahalli has become the fastest growing career path for the individuals. There has been a growing demand for Data Science specialists and Big Data analysts. Data Science has revolutionized the world of the IT sector and has turned out to be a building block for every business organization.
DataMinax ,Data science course in Marathahalli provides you the Real time course environment which leads to explore the knowledge & crack the interviews.
- Lectures 130
- Quizzes 0
- Duration 1 hours
- Skill level All level
- Language English
- Students 24
- Assessments Yes
Part 1: Introduction to Data Science/Analytics
1. INTRODUCTION TO DATA MANAGEMENT
- i. Data Science – Brief
- Data Science Languages – Excel, SQL, Python & Tableau, R.,
- Data Preparation Techniques
- Sample Use Case
- 2. STATISTICS AND EDA
- ii. Introduction to Statistics
- Population and Samples
- Descriptive vs Inferential Statistics
- Parameters vs Statistics
- Variable Classification
- Scale of Measurement
- Statistical Methods
- iii. Data Visualization
- Recognize difference between grouped and ungrouped data
- Construct Frequency Distribution
- Construction of bar diagram, column diagram, histogram,
- frequency polygon, pie chart scatter plot, bubble chart
- iv. Exploratory Data Analysis
- Measure of Shapes of Data
- Measure of central tendency for grouped & ungrouped Data
- Measures of Variability for Grouped and Ungrouped Data
- Various Outlier & Missing value treatments in data preparation
- Relationship between variables
- Correlation and causation.
Part 2: Introduction to R/Python
- Installing Python & Anaconda
- Basic Data Types
- Lists, Tuples & Dictionary
- Introduction to Numpy
- Numpy arrays and operations
- DataFrames & Series
- Group By with Pandas
- Merging, Joining, Concatenating & Interleaving
- Pandas Operations
- Plots with matplotlib and Seaborn
- Preprocessing with dataset
- Case Study
Part 3: Distributions & Sampling
Part 4: Hypothesis Testing
Part 5: Machine Learning
- 1. Predictive Analysis – I
- i. Linear Regression
- Nature of Regression Analysis
- Meaning of the term linear
- Linearity in variables
- Linearity in parameters
- Method of Ordinary Least Square
- The classical linear regression model
- Standard errors of least square estimates
- The coefficient of determination r ^2
- R square and adjusted r square
- Goodness of fit
- The normality assumption
- Hypothesis testing
- Testing the overall significance of a multiple regression – F test
- Multicollinearity assumptions
- Case study : Housing dataset
- ii. Logistic Regression
- Dichotomous dependent variable
- Odds and Odds Ratios
- The Logit Model
- Weight of Evidence and Information value
- Creation of Dummy Variable
- Parameter Estimates
- Goodness of fit, Concordance and discordance
- confusion matrix
- Sensitivity, Specificity , Information Gain
- ROC curve
- Case study : Prediction of Loan Dataset.
- iii. Clustering
- Cluster analysis intuition
- Types of Clustering
- K-Means Clustering
- Case Study
- iv. Principle Component Analysis
- 2. Predictive Analysis – II
- i. MODEL SELECTION AND ADVANCED REGRESSION
- ii. DECISION TREES
- Random Forest regression
- Bagging and boosting
- Gradient Boosting
- iii. NEURAL NETWORKS
- iv. TIME SERIES
- Stationary and Non Stationary Time Series
- Importing and cleaning
- Moving Averages
- Prediction (Holt Winters Method, MA, Exponential Smoothing)
- ARMA model
- Case Study
Part 6: Big Data – Overview
Part 7 : Deep Learning & NLP Overview
- 1. NATURAL LANGUAGE PROCESSING
- BASICS OF TEXT PROCESSING
- LEXICAL PROCESSING
- SYNTAX AND SEMANTICS
- OTHER PROBLEMS IN TEXT ANALYTICS
- 2. DEEP LEARNING & NEURAL NETWORKS
- INFORMATION FLOW IN A NEURAL NETWORK
- TRAINING A NEURAL NETWORK
- CONVOLUTIONAL NEURAL NETWORKS
- Use CNN’s to solve complex image classification problems
- RECURRENT NEURAL NETWORKS
- Study LSTMs and RNN’s applications in text analytics
- CREATING AND DEPLOYING NETWORKS USING TENSORFLOW AND KERAS
- Build and deploy your own deep neural networks on a website, learn to use tensorflow API and Keras
Part 8: Industry/Capstone Projects (Optional)