Data Science Course

Data Analysis and Management

Data science course is now a been top of the mind course for learning and development. It’s known as the sexiest job of the era. In the century of 2021, this emerging field has become a necessity for all businesses and evolving data driven outcomes on the entire globe.

Talent acquisition team of each top companies looking for the best data scientist for their organization who can help them to overcome the talent shortage and create competitive advantages from within.

It has been observed that the data scientist job became the fastest growing jobs around the globe.

Because algorithms, statistics, and analysis are all such integral parts of data science, it’s all too common for data science jobs to be conflated with machine learning skills. In reality, machine learning is one of many key capabilities for data scientists. With machine learning, systems take datasets and run them through models to refine algorithms and generate more effective results. Data science utilizes these algorithms and the benefits of machine learning but isn’t always an automated process. Rather, data science is a broader spectrum that includes data integration, architecture, visualization, business intelligence, decision-making, predictive analytics, and more. As you build out your data science training program, don’t stop short with machine learning courses because they’ve grown so popular. Consider the full spectrum of data science functions.

Key Skills for Data Science Courses

The data science courses are require soft skills and technical skill to success in this field
You must have strong and good command over excel, VBA, SQL, Tableau, Python
You should also be aware about statistical methods to adopt in your models

What you'll learn in Data Science course

Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn

Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow

Impress interviewers by showing an understanding of the data science field
Learn how to pre-process data
Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
Start coding in Python and learn how to use it for statistical analysis
Perform linear and logistic regressions in Python
Carry out cluster and factor analysis

Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
Apply your skills to real-life business cases
Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
Unfold the power of deep neural networks
Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance