Become a true data scientist & machine learning expert with full industry knowledge Apply different predictive models and machine learning algorithms into use cases in different business areas Present analytical results to various users Master Text Mining & Natural Language Processing (NLP) using Python & Spark for sentimental analysis Work on Python with SQL on SQLite, Redshift, SAS, MongoDB, Spark and other data sources Become industry expert in banking, marketing, credit risk and product-user recommender system Collect and analyze Big Data in different systems Use AWS and Azure for Cloud Computing Master fundamental Python programming Apply generic Object Oriented Programming (OOP) Conduct real world capstone projects to build up career path Master useful data engineering knowledge and skills Convert homework and practices into your own knowledge and skills Use all famous graphics tools such as matplotlib, plotly, seaborn and ggplot into data visualization Any one should be able to use computer including being able to install software Desire to learn Python, Data Science and Cloud Computing Prior exposure to programming languages will be helpful Basic knowledge and skills of math In this nearly 50 hours course, we will walk through the complete Python for starting the career in data science and cloud computing! This is so far the most comprehensive guide to mastering data science, business analytics, statistical tests & modelling, data visualization, machine learning, cloud computing, Big data analysis and real world use cases with Python. Data science career is not just a traditional IT or pure technical game – this is a comprehensive area, and above all, you must know why you conduct data analysis and how to deploy your results to generate values for the company you are working for or your own business. Therefore, this course not only covers all aspects of practical data science, but also the necessary data engineering skills and business model & knowledge you need in different industries. Whether you are working in financing, marketing, health companies, or you are running start-up, knowing the complete application of Python for data science and cloud computing is the must to achieving various business objective and looking insights into data.
In an industry that is experiencing a steady rate of job creation, data science itself has moved from just a buzzword to a strategic component in organisations. In addition to this, data scientists are increasingly taking on more strategic roles as organisations employ a product-centric view of data. It is a field that promises tremendous job growth and higher earning potential. Our latest research posits 97,000 jobs are available in this buzzing field. On the hiring end, there is a significant overall growth in jobs in the field.
Anyone who's deeply involved in the tech world has surely heard of the terms Big Data, Data Science, and Machine Learning (ML). Ever since the Digital Revolution (being brought about by a gigantic amount of data) has taken the technological industry by storm, these concepts have been making headlines, and rightly so. Today, the world is sitting over a data goldmine (IBM maintains that every day we create around 2.5 quintillion bytes of data!). And organizations across all parallels of the industry are becoming increasingly reliant on data to drive business decisions to foster innovation and development. Consequently, job opportunities are escalating rapidly.
Design and build personalization engines/learning systems using advanced machine learning and statistical techniques Help the company in identifying tools and components, and building the infrastructure for AI/ML Research and brainstorm with internal partners to identify advanced analytics opportunities to advance automation, help with knowledge discovery, support decision-making, gain insights from data, streamline business processes, and enable new capabilities Perform hands-on data exploration and modeling work on massive data sets. Perform feature engineering, train the algorithms, back-test models, compare model performances and communicate the results Work with senior leaders from all functions to explore opportunities for using advance analytics Provide technical leadership mentoring to talented data scientists and analytics professionals Guide data scientists and engineers in the use of advanced statistical, machine learning, and artificial intelligence methodologies Provide thought leadership by researching best practices, extending and building new machine learning and statistical methodologies, conducting experiments, and collaborating with cross functional teams Develop end-to-end efficient model solutions that drive measurable outcomes. These technical skills include, but not limited to, regression techniques, neural networks, decision trees, clustering, pattern recognition, probability theory, stochastic systems, Bayesian inference, statistical techniques, deep learning, supervised learning, unsupervised learning Solid understanding and hands on experience working with big data, and the related ecosystem, both relational and unstructured. Executing on complex projects, extracting, cleansing, and manipulating large, diverse structured and unstructured data sets on relational – SQL, NOSQL databases Working in an agile environment with iterative development & business feedback Providing insights to support strategic decisions, including offering and delivering insights and recommendations Experience in statistics & analytical modeling, time-series data analysis, forecasting modeling, machine learning algorithms, and deep learning approaches and frameworks. Deliver robust, scale and quality data analytical applications in a cloud environment.
Modern technologies like artificial intelligence, machine learning, data science have become popular but no one completely understands it. They appear to be extremely complicated to a layman. All these popular terms sound like a business executive or a student from a non-technical background. People often get confused by words like AI, ML and data science. In this blog, we clarify these technologies in basic words so you can easily distinguish them and how they are being used in business.