Data Science training in BTM, Bangalore
Data Science Training In BTM
Data science is one of the best-chosen options for students who know the technology. Data science is for the students who are seeking for python, R, Tableau etc. and willing to make it as their career option. Students can indulge in the IT sector with this and can also become a data scientist. In this new technical world it is necessary to take steps with it hence you can increase your capabilities and skills by taking high training program and become a data science expert.
Benefits Of Opting For Data Science Training In BTM
Check out the advantages of taking up a data science training in BTM:
1) The training makes the students evaluate and work on their skills.
2) A small number of students in the class help the teachers to have an eye on everyone so that no student is left behind.
3) A well-experienced staff promotes self-confidence which is quite beneficial for project handling in the future.
4) Facility of A+ class placements and guidance.
5) Daily availability of projects and Target to shape up your skills.
6) Helps in building self-confidence, teamwork, leadership quality, Communication skills etc.
Data Minax Institute: Best Data Science Training Institute in BTM
Data Minax is a top data science training institute in BTM that provides an endless array of opportunities to all the students who are dreaming to achieve big in this field. The trainers train a student in such a way that the student can understand the industry-specific problems in a better way. The institute also provides a good atmosphere with deep analysis of the data science which makes this training institute a provider of the ultimate data science training in BTM. Get a pure blend of human expertise and computer knowledge to learn all the ins and outs in this field.
Why Choose Data Minax For Data Science Training In BTM
Data Minax uses a practical approach rather than theoretical in all its data science courses in BTM. There are a number of reasons to choose Data Minax over any other institute for your data science training in BTM:
1) The institute provides a full learning package so that you can make a grip upon the theoretical knowledge.
2) They have a small bunch of students in every batch so that they can get expertise to keep track of the performance of all of their students.
3) Computer lab facility to practice and groom their skills.
4) Experienced staff with a keen interest to make every student attain a better future.
5) Future internships programs are also available.
6) Different time slot availability so that one can choose a preferable time.
7) A good fee structure which is genuine and quite affordable.
Thus, owing to these reasons, Data Minax is a perfect platform for the students to make their data science skills sharpen. They train students who can serve well in the IT sector. A platform which enables its students a better data scientist and becomes an analytical expert in the field of data science.
- Lectures 130
- Quizzes 0
- Duration 1 hours
- Skill level All level
- Language English
- Students 12
- 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)