Welcome to Online Training from India
Data Science Online Training from India
Data Science Online Training Course Content
Course Duration : 35 to 40 Hrs
Linux_OS_Basics and Shell Scripting
1. Data Wandering
1. STASTISTICS
1. SUPERVISED LEARNING AND MODEL BUILDING
- Linux_Commands
- File_System
- vi editor
- Advanced_Linux_Commands
- System_Administration
- Advanced_System_Administration
- Grep
- Shell_Scripting with examples(All types of loops)
- Regular_Expressions
- SED
- AWK
MODULE 1: Introduction To Python - Data Science
- Installation of Anaconda setup (Data Science Development Environment)
- Installation of Pycharm
- Working with Python List , List operation , Functions
- Python Tuple , working and functions
- Sets and Dictionary -operations and Working with them
- Python More on Strings
- Python Dates and Times
- More on functions
- Advanced Python Lambda
- List Comprehensions
MODULE 2: Data Analysis
1. Data Wandering
- All about files Files
- importing and exporting data with CSV files
- XLRD module - working with xls .xlsx formats
- Json data
- XML data
- Relational data Bases
- Sql in python
- Data quality Analysis
- strong>Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)
- Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)
- Python Built-in Functions (Text, numeric, date, utility functions)
- Python User Defined Functions
- Stripping out extraneous information
- Normalising data
- Formatting data
- Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc)
- Introduction exploratory data analysis
- Descriptive statistics, Frequency Tables and summarization
- Univariate Analysis (Distribution of data & Graphical Analysis)
- Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
- Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
- Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, seaborn, Pandas and scipy.stats etc)
- The Series Data Structure
- Querying a Series
- The Data-Frame Data Structure
- Data-Frame Indexing and Loading
- Querying a Data-Frame
- Indexing Data-frame
- Understanding business problem
- Selecting columns from Pandas Data Structures
- Treating with missing values, outliers, NaN values
- Creating new columns
- Aggregate data ( use: groupby, merge, pivot, lambda)
- Identifying unique values in data
- Filter Data
- Using basic functionality of Pandas API
MODULE 3: Mathamatics
1. STASTISTICS
- Basic Statistics - Measures of Central Tendencies and Variance
- Building blocks - Probability Distributions - Normal distribution - Central Limit Theorem
- Inferential Statistics -Sampling - Concept of Hypothesis Testing
- Statistical Methods - Z/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square
- Important modules for statistical methods: Numpy, Scipy, Pandas
- Probability , Conditional Probability
- Basic of Probability, Independent and Dependant events
- Conditional Probability and Bayes Theorem
- Continuous Probability Distributions
- Mean, Median, Mode, Range
- Determination of statistical techniques
- Standard Deviation, Variance, Covariance, Correlation
- outliners
- Distribution of Data – Normal, Binomial, Gaussian
- Different types of Data
- Continuous , Categorical, Range
- Testing of Hypothesis – which covers
- Level of Significance (LOS), Level of Confidence, P-Value, T test, Z-test, ANOVA Test, CHI -Square Test
MODULE 4: Machine Learning
1. SUPERVISED LEARNING AND MODEL BUILDING
- Process of Machine Learning
- Model Building based on Data sets
- Splitting Data: Training and Test sets
- Regression Analysis (Linear, Multiple, Logistics Regression)
- Classification concepts and Distance Functions
- K-nn Algorithm concept and demonstration with data sets
- Bayes Classification concept and demonstration with data sets
- Decision Tree Algorithm concept and demonstration with data sets
- Random Forests - Ensembling Techniques and Algorithms
- Unsupervised Learning and Clustering Techniques
- Centroid-based Clustering: K- Mean Algorithm concept and demonstration
- Hierarchical Clustering concepts and Applications
- Density-based Clustering: DBSCAN Algorithm concept and demonstration
- Dimension Reduction Introduction
- Why Dimension Reduction Required
- LDA (Linear Discriminant Analysis) concept and applications
- PCA (Principle Component Analysis) concept and applications
- Introduction - Applications
- Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition
- Classification of Techniques(Pattern based - Pattern less)
- vBasic Techniques - Averages, Smoothening
- Advanced Techniques - AR Models, ARIMA
- Applying different algorithms to solve the business problems and bench mark the results
We are providing Data Science Online Training from Hyderabad India. We are one of best Institute to provide High Quality Data Science online training all over India. If you are staying in Hyderabad, Bangalore, Chennai, Pune, Delhi, USA, UK, Australia, Singapore etc. and unable to attend regular class room training programs then contact our training institute for information on online training. For more details on Data Science Online Training please call to 9290971883, / 9247461324, or drop a mail to revanthonlinetraining@gmail.com
Data Science online training institute address : B1, 3rd Floor, Eureka Court, Near Image Hospital, Ameerpet, Hyderabad, India
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