Best Data Science Training with Python in Bangalore
Data science is a process of studying the Raw Data, it involves Various Statistical Measures & developing Methods of recording, Storing & Analyzing the Data for Extracting the Useful Information. The main Aim of Data Science is to get insights and Knowledge from both Structured & Unstructured Data. Data science is more closely related to the mathematics field of Statistics, which includes the collection, organization, analysis, and presentation of data. Because of the large amounts of data companies and organizations maintain, Data science has become an integral part of IT
Data scientists are the no. 1 most promising job in America for 2019, according to a Thursday report from LinkedIn.
Data Science Can be Considered as the Fuel for all the Industries, with the Latest Technology & Inventions, Data Science finds its Separate path to prove the success
No Doubt Data Science is Very Important, but it is a Word often Misunderstood. Since it is a Business word its easy to dismiss, but Data Science is Absolutely important. Data Science Includes Very Specific Set of Activities & skills that businesses can leverage to there Success, Data Science allows the business to Process Data at their Disposal, Based on the Type such as Financial Data, Customer Data in an intelligent manner.
Data Science helps the Business to Arrange the Data in a proper sequential manner according to the type of data and thereby helping the business to take necessary actions in Proper Sequence
- All Industries need Data to help them in taking the Correct Decisions & Data Science allows raw data to a neat & processed data to provide meaningful reports.
- Data Science finds its applications in almost all Domains it can be in Health Care, Financial Companies, bank, Travel industries & lot more which in turn provides boost to Data Science
- New update for the Company is Data, & Necessary innovations are taken care for the procuring the correct Data. Industries undoubtedly require the data scientists to for handling the large data & help the companies in taking smart decisions
- Data Scientist
- Data Engineer
- Data Architect
- Machine Learning Engineer
- Application Architect
- Statistician & Lot More
- Designed with the team of Experts to Provide the Efficiency
- Timely doubt Clarification
- Caddgild Allumni Status
- Dedicated Student Success Mentor
- 5+ Project & case Studies
- Flexibility in Paying the Fees
Introduction to Data Science and Statistical Analysis
- Introduction to Data Science
- The Need of Business Analytics
- Data Science Life Cycle
- Various Tools Available for Data Science
Introduction to Programming Tool- Python
- Introduction to Python
- Variables, Operators & String Methods
- Decision Making, Loops, Lists & Enumerate
- Analyzing & manipulating Data
- Multi-threading, Thread, Threading Module, Python debugger, Unit test, Python project structure, SQLite, DB, Tables & CRUD operations.
- OOP Concept, Classes, Objects, Methods, Garbage collection, Inheritance, Overriding & Data hiding
- Sorting, List sort, Modules, Regular expressions & search & replace
- Merge Multiple Data Set
- Terminologies of Statistics
- Measures of Centers, Measures of Spread
- Descriptive Statistics
- Inferential Statistics
- Normal Distribution
- Hypothesis Testing
- Anova & Histogram
- Chi-Square Test & Anova
Data Exploration, Data Wrangling, and Python Data Structure
- Importing and Exporting data from an external source
- Data exploratory analysis
- Functions, Apply Functions
Pandas Data Frame
- Creating a Data Frame
- Dealing with rows & Columns
- Indexing & Selecting Data with Pandas
- Working with Missing data
- Working with Time & Date
- Working on data Cleanup Tolls
- Mathematical function
- Sting Operation
- Array Creations
- Binary Operation
- Sorting, Searching & Counting
- Data type object in Numpy
- Set 1 & Set 2 (Introduction & Advanced)
- Multiplication Using Numpy
- Bar Graph (Simple, Grouped, Stacked)
- Pie Chart, Line Chart, Bar Graph, Scatter plot
- Introduction to Machine Learning
- Applications of Machine Learning
- Staistics, probability, Hypothesis Testing
- Multiple Linear Regression & Quadratic Regression Analysis
- Introduction to Random Forest
- Unsupervised Learning Techniques, HierarchialClustering
- K Means Clustering
- Model Evaluation Clustering
- What are Classification and its use cases
- What is Decision Tree
- Algorithm for Decision Tree Induction
- Creating a Perfect Decision Tree Confusion Matrix
- Bagging, Boosting & Types of Boosting
Big Data & Hadoop
- Components in Big data
- Architecture of Big Data
- Apache Spark Components
- Map Reduce
- RDD & PySpark
- Weekday batch: conducted 5 days/week/ 2 hrs.
- Weekends batch: conducted Sat-Sun/ 4 hrs.
Most Popular Courses
Tools Used In Data Science Training
Introduction to Data Science and Statistical Analytics
Introduction to Python
- Variables, types, Operators & String methods Variables, types, Op...
- Decision making, Loops, Lists, Tuples & enumerate Decision making, Loo...
- Multi-threading, Thread, Threading Module, Python debugger, Unit test, Python project structure, SQLite, DB, Tables & CRUD operations. Multi-threading, Thr...
- OOP Concept, Classes, Objects, Methods, Garbage collection, Inheritance, Overriding & Data hiding OOP Concept, Classes...
- Sorting, List sort, Modules, Regular expressions & search & replace Sorting, List sort, ...
Data Exploration, Data Wrangling, and R Data Structure
Introduction to Statistics
- What are Classification and its use cases? What are Classificat...
- What is Decision Tree? What is Decision Tre...
- Algorithm for Decision Tree Induction Algorithm for Decisi...
- Creating a Perfect Decision Tree Confusion Matrix Creating a Perfect D...
- Bagging & Boosting & Types of boosting Bagging & Boost...
Big Data & Hadoop
Imbalance classes, Under sampling, Oversampling, Synthetic Data generation, CSL, Accuracy measures,Confusion matrix and Grid search
Final Project Assignment submission & review Part-1
Final Project Assignment submission & review Part-2