Best Data science Training Institute in Hyderabad--AcuteLearn Technologies.

AcuteLearn will provide Data Science Corporate and online training.

Data science with Python Training

This course provides a training from basics of Data science on Advanced data science course which includes R, Python, Hadoop, Statistics, Machine Learning, Deep Learning using Tensor flow and Keras, Computer Vision, Neural Networks . The course allows one to bring up their basic data base knowledge and make it apply to the more advanced level of data science which is a very much typically needed mindset for the current data analysis of IT field.

Course content

Elementary concepts in computation

• Computational model building: Problem statement, abstraction, algorithm, flow charts
• Program and simulations.
• Examples of model building: Value of pi, generating normal distribution.
• Software development: Requirement, design, implementation, testing and maintenance.
• Programing languages: Types, examples, compilers and interpreters.
• Limits of numerical computation: Precision, accuracy and errors.

Introduction to Python I

• What, why and where of Python.
• Installing interpreters and IDEs
• Execution modes of Python: Interactive Read-Eval-Print-Loop mode and script mode.
• Basic Instructions: Input, output, comments, doc strings etc.
• Data objects, basic data types, naming rules and associated syntax.
• Assignments

Introduction to Python II

• Basic Sequence types (Lists, strings, tuples etc.,), their uses, and associated syntax.
• The Syntax of basic operations (slicing, copying, numerical, Boolean and logical).
• Conditional expressions and control flows (if, else, while, for, assert).
• Modules and Functions.
• Some useful library functions (Sum, average, type conversion, etc.,).
• Assignments

Introduction to Python III

• Object orientation and programming.
• Concepts: Class, Methods, object manipulation, instance, inheritance and hierarchy.
• OOP implementation and examples.
• Recursions: Iterators and generators.
• Introducing 6 important Python Libraries for Data Science (Numpy, Pandas, Scipy, etc.,).
• Assignments.

Introduction to Jupyter Notebook (for Python)

• Introducing Interactive computing environment (Specifically Jupyter Notebook).
• Installing and launching Ipython Notebook.
• Structure of a notebook and workflow (Dashboard, UI, and Editor).
• Cell types, Importing and saving in other formats (HTML, Latex etc.,).
• Example notebook implementations.
• Assignments

Essential Math and Statistics with Python I

• Equations, Functions, Graphs, vectors and Matrices.
• Population, Sample, observations and variables.
• Descriptive statistics 1: Frequency distribution and graphical representation.
• Descriptive statistics 2: Measures of central tendency, dispersion, box plots and
• other representations.
• Association between variables, correlation coefficients and graphical
• visualization.
• Assignments.

Essential Math and Statistics with Python II

• Permutations, combinations, set theory, conditional probability and bayes theorem.
• Random numbers and their generators.
• Confidence intervals, hypothesis testing, and Type I & type II errors.
• T-test, z-test, chi-squared test and f-test.
• Regression: Linear, multiple linear, logistics regression etc.,
• Assignments.

Introduction to data wrangling and descriptive analysis.

• Revisiting Pandas, Numpy and other data manuplation tools.
• Gathering data, Assessing data and Cleaning data.
• Data treatment (Missing values and other changes).
• Learning to communicate findings about data: revisiting Data visualisation.
• Assignment.

• Basics of Databases
• Relational databases and Examples of Databases systems.
• Installing SQLite and basic Basic SQL database query language.
• Database queries in Python.
• Creating and manipulating a Database.
• Assignments.

Advanced Analytics and Data modeling

• Introduction to predictive modeling and decision making.
• Parametric vs non parametric modeling.
• Supervised vs unsupervised learning.
• Deep Introduction to Regression problems with examples.
• Deep introduction to Classification problems with examples
• Assignments.

Dimensionality reduction and unsupervised learning with Python

• Machine Learning tools and libraries in Python.
• Cluster analysis: K-means and hierarchical.
• Implementation examples of K-means and hierarchical clustering
• Dimensionality Reduction : SVD, PCA, LDA and MDL.
• Implementation examples of SVD and PCA.
• Assignments.

Supervised learning with Python I

• Contrasting classification and regression.
• Generalized Linear Models.
• Naive Bayes.
• Nearest Neighbours.
• Support vector machines
• Assignments.

Supervised learning with Python II

• Decision Trees.
• Introduction to ensemble methods.
• Bagging, Boosting and voting classifiers.
• Adaboost technique.
• Random forrest method.
• Assignments.

Supervised learning with Python II

• Introduction to Model selection.
• Deeep introduction to Evaluation parameters.
• Classification Metrics (Accuracy score, confusion matrix, hamming loss, log loss etc.,).
• Regression Metrics (Mean absolute error, mean squared error and R-squared error).
• Cross Validation and tuning of model hyperparameters.
• Assignments.

Neural Networks and Deep learning (Optional)

• Basic introduction of Neural networks.
• Implementing a shallow neural network with Python.
• Introduction to Back propagation and Deep learning.
• Deep Learning with Python: Tensorflow Library.
• Applications and criticism.

For more information please do reach us +91-7702999361/371, [email protected], www.acutelearn.com

Others Details

Acutelearn is leading training company provides corporate and online trainings on various technologies like AWS, Azure, Blue prism, CCNA, CISCO UCS, CITRIX Netscaler,CITRIX Xendesktop, Devops chef, EMC Avamar, EMC Data Domain, EMC Networker, EMC VNX, Exchange Server 2016, Hyper-V, Lync server, Microsoft windows clustering, Netapp, Office 365, Red Hat Linux, openstack, SCCM, vmware nsx 6.0, vmware vrealize, vmware vsphere, windows powershell scripting, DataScience, Azure Data & Analytics.