 # Best Data Science Training Institute in Marathahalli

### Complete Data Science Course

#### INTRODUCTION

• Need of Data analyses
• Need of Statistics
• How Visualization helps Industry?
• How models helps to predict and find the pattern in it
• Data Science tools in market
• Why Python/R for Statistics & Data science?
• Who analyze data?
• #### Statistics

• Sample vs Population
• Descriptive & Inferential statistics
• Central tendency (Mean, Median, Mode)
• Standard deviation, variance, Quartiles, Box Plot
• Hypothesis testing
• Normal distribution, uniform distribution
• Histogram, frequency distribution
• Poisson distribution
• P-value, Z-test, T-test, F distribution
• Type 1 and 2 errors
• Chi square test
• Annova Analyses
• R-Square, Adjusted R-Square

#### CHARTS

• Table visualization
• Cross Table
• Graphical table
• Bar chart
• Waterfall chart
• Line chart
• Combination chart
• Pie chart
• Scatter plot
• 3D Scatterplot
• Map chart
• Tree Map
• Heat Map
• KPI Chart
• Parallel co-ordinate plot
• Summary Table
• FEATURES
• Information designer
1. Data source
2. IL’s – (prompts, parameterized, personalized)
3. Joins
4. Procedures
• Library Management
1. GUID
2. Export/import elements
• Text Area
• Data table/column/document properties
• Data on demand
• Markings, Calculated columns, Transformations
• Filters (types, schemas, filter functions)
• Details on Demand
• Tags, Lists, Bookmarks, Collaboration
• Webpage, Recommended visualizations
• Export visualizations
• Python Or / And R
PYTHON
• Basics of Python
• Anaconda tool
• Spyder & Jupyter Notepad
• Arrays
• Dataframes
• Visualizations
• Pandas
• Numpy
• Matplotlib
• Seaborn
• All Packages related to Data science.
• R
• Basics of R
• R Studio tool
• Vectors
• Lists
• Matrices
• Arrays
• Factors
• Data frames
• Visualizations
• All packages related to Data Science

## Basic R with statistics in-detailed

Slno Topic Details
1. Introduction of R Evolution, need, trend, features
2. Installation & R studio navigation Installation commands, R studio UI walk through
3. Variables, constants Variables, constants, declarations, intializations
4. Data Types Numbers, boolean, String, Date, posixct
5. Data Structures Arrays, Lists, vectors, Matrices, Data frames
6. Sequence Generation Random Numbers, Samples
7. Conditional statements, Loops & Operators Basic operators & statistical functions
8. Functions Create/Application/usage
9. Different ways to Import and Export data into/from R
10. Data Transformations and cleaning tidy Package usage
11. Basics of Visualizations scatter, Box, Bar, Pie, etc..
12. Layered graphics with gglot2
13. R statistical Analysis mean, median, variance, standard deviation, etc..
14. R Visualizations of distributions Normal, hypothesis, p-value, all statistical methods
15. Outliers and missing data analysis

### Machine Learning

• Introduction to data science
• Exploratory data analysis
• Introduction to machine learning – Supervised/Unsupervised
• Linear regression and regularization
• Model selection and evaluation
• Classification: KNN, decision trees, SVM
• Ensemble methods: random forests
• Intro to probability: Naive Bayes and logistic regression
• Clustering: k-means, hierarchical clustering
• Dimensionality reduction: PCA and SVD
• Text mining and information retrieval
• Network Analysis
• Recommender systems
• Model Validation
• NLP Image Processing Deep learning
• NLP
• Sentiment analyses
• Tf-idf for search engines
• Reading the image
• Smoothening of image techniques(Median, Average, Gaussian, Bilateral)
• Edge detection methods (Sobelx, Sobely, Laplacian, Canny)
• Drawing Contours
• Zoom in, Zoom out, Negative imaging
• Contrast methods (logarithmic, Power, Histogram equalization)
• Neural networks (NN)
• Convolutional neural networks (CNN)
• Back propagational neural networks (BpNN)
• Projects
• Complete Health care clinical trials knowledge will be delivered.
• Datasets related to clinical domain will be discussed (Subject Demographics, Adverse events, Enrollment, Inclusion exclusion, Screenfailure, Informed consent, Lab results, Vital signs, tumor related data, cardiovascular data, sites (hospitals), etc..)
• Clinical trials client requirements will be discussed and solved using all concepts of the course such as Statistics, Spotfire, R or Python, Machine learning, Deep learning, NLP, image processing.
• Other generic examples will also be discussed such as digit recognition (Image processing), etc.